Python Developer Jobs in Qatar

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Mindrift is looking for highly skilled Vibecode specialists to join the Tendem project (https://tendem.<br>ai/) and drive specialized data scraping workflows for real-world use cases.<br> Mindrift is looking for highly skilled Senior Python Data Scraping Engineers to join the Tendem project and drive specialized data scraping workflows for real-world applications.<br> In this role, you'll apply your expertise in web scraping, data extraction, and data processing to deliver accurate, reliable, and high-quality results.<br> This part-time remote opportunity is ideal for technical professionals with hands-on experience in web scraping, data extraction and processing.<br> What We Do The Mindrift platform connects specialists with innovative technology projects.<br> Our mission is to help develop high-quality AI technologies by combining real-world expertise from professionals across the globe with advanced AI development efforts.<br> About the Role This is a freelance role for a Tendem project.<br> As a Senior Python Data Scraping Engineer , you'll handle data scraping tasks requiring technical precision for web extraction and processing, utilizing tools such as Apify, OpenRouter, and other technologies, alongside your own technical expertise and approaches.<br> Key Responsibilities Own end-to-end data extraction workflows across complex websites, ensuring complete coverage, accuracy, and reliable delivery of structured datasets.<br> Leverage available tools and custom workflows to accelerate data collection, validation, and task execution while meeting defined requirement.<br> Ensure reliable extraction from dynamic and interactive web sources, adapting approaches as needed to handle JavaScript-rendered content and changing site behavior.<br> Enforce data quality standards through validation checks, cross-source consistency controls, adherence to formatting specifications, and systematic verification prior to delivery.<br> Scale scraping operations for large datasets using efficient batching or parallelization, monitor failures, and maintain stability against minor site structure changes.<br> Educational qualifications At least 5+ years of relevant experience in data engineering, web scraping, automation, or software development (required).<br> Bachelor’s or Master’s Degree in Engineering, Applied Mathematics, Computer Science, or related technical fields is a plus.<br> Academic and/or Professional Experience Candidates should have a strong technical foundation and practical experience with scripting, automation, and data extraction workflows.<br> We are looking for specialists who can solve non-trivial problems, work confidently with modern development tools and technologies, and systematically collect, structure, and validate data from diverse sources.<br> A methodical, detail-oriented approach and the ability to work independently are essential.<br> Technical Skills (Essential) Strong experience in Python web scraping (BeautifulSoup, Selenium or similar), including dynamic content (JS, AJAX, infinite scroll) and APIs via proxies Proven ability to extract data from complex structures (hierarchies, archived pages, inconsistent HTML) Solid background in data cleaning, normalization, and validation, delivering structured datasets (CSV, JSON, Google Sheets) Additional requirements Demonstrated experience handling anti-bot mechanisms and dynamic site structures at scale Experience with cloud infrastructure (AWS or equivalent) and containerization (Docker) as part of real workflows Hands-on experience with LLM frameworks (LangChain, OpenRouter, or similar) applied to automation tasks Strong attention to detail and commitment to data accuracy Self-directed work ethic with ability to troubleshoot independently A link to GitHub is a plus English proficiency: Upper-intermediate (B2) or above (required) Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements.<br> This is an estimate, not a guaranteed workload, and applies only while the project is active.<br> Compensation On this project, contributors can earn up to $37 per hour equivalent , depending on their level and pace of contribution.<br> Compensation varies across projects depending on scope, complexity, and required expertise.<br> Please note that other projects on the platform may offer different earning levels based on their requirements.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Mindrift is looking for highly skilled Vibecode specialists to join the Tendem project (https://tendem.<br>ai/) and drive specialized data scraping workflows for real-world use cases.<br> Mindrift is looking for highly skilled Senior Python Data Scraping Engineers to join the Tendem project and drive specialized data scraping workflows for real-world applications.<br> In this role, you'll apply your expertise in web scraping, data extraction, and data processing to deliver accurate, reliable, and high-quality results.<br> This part-time remote opportunity is ideal for technical professionals with hands-on experience in web scraping, data extraction and processing.<br> What We Do The Mindrift platform connects specialists with innovative technology projects.<br> Our mission is to help develop high-quality AI technologies by combining real-world expertise from professionals across the globe with advanced AI development efforts.<br> About the Role This is a freelance role for a Tendem project.<br> As a Senior Python Data Scraping Engineer , you'll handle data scraping tasks requiring technical precision for web extraction and processing, utilizing tools such as Apify, OpenRouter, and other technologies, alongside your own technical expertise and approaches.<br> Key Responsibilities Own end-to-end data extraction workflows across complex websites, ensuring complete coverage, accuracy, and reliable delivery of structured datasets.<br> Leverage available tools and custom workflows to accelerate data collection, validation, and task execution while meeting defined requirement.<br> Ensure reliable extraction from dynamic and interactive web sources, adapting approaches as needed to handle JavaScript-rendered content and changing site behavior.<br> Enforce data quality standards through validation checks, cross-source consistency controls, adherence to formatting specifications, and systematic verification prior to delivery.<br> Scale scraping operations for large datasets using efficient batching or parallelization, monitor failures, and maintain stability against minor site structure changes.<br> Educational qualifications At least 5+ years of relevant experience in data engineering, web scraping, automation, or software development (required).<br> Bachelor’s or Master’s Degree in Engineering, Applied Mathematics, Computer Science, or related technical fields is a plus.<br> Academic and/or Professional Experience Candidates should have a strong technical foundation and practical experience with scripting, automation, and data extraction workflows.<br> We are looking for specialists who can solve non-trivial problems, work confidently with modern development tools and technologies, and systematically collect, structure, and validate data from diverse sources.<br> A methodical, detail-oriented approach and the ability to work independently are essential.<br> Technical Skills (Essential) Strong experience in Python web scraping (BeautifulSoup, Selenium or similar), including dynamic content (JS, AJAX, infinite scroll) and APIs via proxies Proven ability to extract data from complex structures (hierarchies, archived pages, inconsistent HTML) Solid background in data cleaning, normalization, and validation, delivering structured datasets (CSV, JSON, Google Sheets) Additional requirements Demonstrated experience handling anti-bot mechanisms and dynamic site structures at scale Experience with cloud infrastructure (AWS or equivalent) and containerization (Docker) as part of real workflows Hands-on experience with LLM frameworks (LangChain, OpenRouter, or similar) applied to automation tasks Strong attention to detail and commitment to data accuracy Self-directed work ethic with ability to troubleshoot independently A link to GitHub is a plus English proficiency: Upper-intermediate (B2) or above (required) Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements.<br> This is an estimate, not a guaranteed workload, and applies only while the project is active.<br> Compensation On this project, contributors can earn up to $37 per hour equivalent , depending on their level and pace of contribution.<br> Compensation varies across projects depending on scope, complexity, and required expertise.<br> Please note that other projects on the platform may offer different earning levels based on their requirements.<br></span> </div>

<p>The primary responsibilities of the Security Infrastructure Engineer Google SecOps:</p><p>Functional Responsibilities:</p><ul><li><strong>Data Ingestion and Normalization Pipeline Management:</strong> Architect and maintain the ingestion of telemetry from multi-cloud (GCP, AWS, Azure) and on-premises environments using Bind Plane Forwarders, Cloud-to-Cloud (C2C) connectors, and Webhooks.</li><li><strong>Parser Development:</strong> Design, build, and troubleshoot custom parsers (CBN) to ensure non-standard log sources are correctly normalized into the Unified Data Model (UDM).</li><li><strong>Data Health Monitoring:</strong> Build dashboards to monitor ingestion rates, latency, and data drops to ensure the SIEM is always receiving high-quality, actionable data.</li><li><strong>SOAR &amp; Automation Engineering</strong></li><li><strong>Playbook Development:</strong> Design and code automated incident response playbooks in Google SOAR using Python and visual builders.</li><li><strong>Connector Engineering:</strong> Build and maintain API integrations between Google SOAR and third-party tools (Firewalls, EDR, IAM, Ticketing systems).</li><li><strong>Workflow Optimization:</strong> Automate repetitive manual tasks such as artifact enrichment, evidence gathering, and initial containment actions.</li><li><strong>Case Management Configuration:</strong> Tailoring the SOAR environment to fit the SOC s operational needs, including custom fields, stages, and SLA tracking.</li><li><strong>Platform Administration and Optimization</strong></li><li><strong>System Health Monitoring:</strong> Monitoring the ingestion health to ensure no data is dropped and that latency stays within acceptable limits.</li><li><strong>Access Control:</strong> Managing Role-Based Access Control (RBAC) to ensure analysts have the correct level of access to sensitive data.</li><li><strong>Threat Intel Ingestion:</strong> Managing the integration of Mandiant, Virus Total, and other third-party threat intelligence feeds to ensure detections are always up to date with the latest global threats.</li><li><strong>Collaboration with SOC Team</strong></li><li><strong>Feedback Loops:</strong> Collaborating with Tier 1 and Tier 2 analysts to tune YARA-L rules based on real-world alert performance and noise levels.</li><li><strong>Requirements Gathering:</strong> Interviewing incident responders to understand their manual workflows, then translating those into Google SOAR playbooks.</li><li><strong>Training &amp; Enablement:</strong> Conducting knowledge transfer sessions on how to use UDM Search and the Google SecOps interface to speed up investigations.</li><li><strong>Alignment with Infrastructure Team</strong></li><li><strong>Data Ingestion Strategy:</strong> Working with GCP/AWS/Azure Architects to ensure that Cloud Logging and Pub/Sub are configured correctly for seamless export to Google SecOps platform.</li><li><strong>Agent Deployment:</strong> Coordinating with IT Infrastructure teams to deploy and maintain Bind Plane Forwarders on on-premises servers and virtual machines.</li><li><strong>Troubleshooting:</strong> Collaborating with Network Engineers to resolve connectivity issues or firewall blocks that prevent telemetry from reaching the Google SecOps platform.</li></ul><p><strong>Desired Candidate Profile</strong></p><h2>Knowledge, Skills &amp; Experience</h2><h3>Academic &amp; Professional Qualifications:</h3><ul><li>Bachelor s degree in computer science, IT, Cybersecurity, or equivalent.</li><li>SIEM Certification ( Google SecOps, Splunk, Azure Sentinel).</li><li>Preferred: Security certifications such as Security+, CySA+, CEH, CISSP, GCIH</li></ul><h3>Google SecOps Engineer Experience:</h3><ul><li>3 5 years of hands-on experience in Security Engineering, SOC Automation, DevOps Engineer, Security Operations, or Infrastructure Security.</li></ul><h3>Skills and Requirements:</h3><ul><li><strong>SIEM/SOAR Mastery:</strong> Proven experience architecting and managing enterprise-grade platforms (e.g., Splunk, Azure Sentinel, or QRadar), with at least 1 2 years specifically focused on Google SecOps (Chronicle). Key Requirement: Required skills: Google SecOps.</li><li><strong>Coding &amp; Scripting:</strong> Professional experience using Python to automate security workflows or build custom API connectors.</li><li><strong>Cloud Infrastructure:</strong> Hands-on experience managing security within Google Cloud Platform (GCP), including VPC service controls, IAM, and Cloud Logging.</li><li><strong>Languages:</strong> Python (Advanced), SQL (BigQuery), YARA/YARA-L, and Bash.</li><li><strong>Frameworks:</strong> MITRE ATT&amp;CK, NIST Cybersecurity Framework.</li><li><strong>Tools:</strong> Git (Version Control), Terraform (Infrastructure as Code), Docker/Kubernetes (Containerization).</li><li><strong>Data Standards:</strong> Deep knowledge of JSON, Protobuf, and Regex for log parsing and normalization.</li></ul><h3>Soft Skills</h3><ul><li>Strong analytical thinking and problem-solving capability.</li><li>Excellent communication skills, able to explain technical findings to non-technical stakeholders.</li><li>Ability to work independently, manage multiple priorities, and meet deadlines.</li><li>Attention to detail and a structured, documentation-driven mindset.</li></ul>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <p>Artefact is a new generation of data service providers specialising in data consulting and data-driven digital marketing. It is dedicated to transforming data into business impact across the entire value chain of organisations. We are proud to say we’re enjoying skyrocketing growth.<br>The backbone of our consulting missions, today our Data consulting team has more than 400 consultants covering all Artefact's offers (and more): data marketing, data governance, strategy consulting, product owner…</p><br><p><strong>What you will be doing?<br></strong>As a Data Engineer, your role involves crafting and maintaining robust data pipelines, utilising Python and SQL, to ensure efficient extraction, transformation, and loading (ETL) of data.</p><br><p>Your responsibilities will include:</p><br><ul><li>Data Pipeline Development: Building and optimising data pipelines to facilitate seamless data flow across systems and platforms.</li><li>Database Management: Managing databases, ensuring their integrity, and implementing data storage and retrieval solutions.</li><li>Cloud Services Integration: Leveraging cloud services such as MS Azure, GCP, and AWS to architect and deploy scalable data solutions.</li><li>Machine Learning Integration: Collaborating with teams to integrate machine learning models into data pipelines for enhanced data processing.</li><li>Utilising Spark &amp;amp; Kafka: Implementing and working with Spark and Kafka for real-time data processing and analytics.</li></ul><p><strong>What we are looking for?</strong></p><br><ul><li>2-5 years: Data Engineer</li><li>Proficiency in Python, SQL, and database management.</li><li>Experience with Data Pipelines ETL, Cloud Services (MS Azure, GCP, AWS), ML Modeling, Spark &amp;amp; Kafka.</li><li>Proven problem-solving skills and a solution-oriented mindset.</li><li>Experience working with business stakeholders either internally or externally</li><li>Excellent communication skills to collaborate effectively within teams and with stakeholders.</li><li>Strong business acumen with an interest in business-facing roles.</li><li>Adaptability and a start-up mentality to thrive in a dynamic environment.</li><li>Minimum of a bachelor's degree in Computer Science, Electronics, and Communication Engineering degrees.</li></ul><p>Artefact is the place to be: come and build the future of data and AI</p><br><p><strong>Innovation:</strong> We have a passion for creating impacting projects and believe innovation can come from anyone.<br><strong>Action:&amp;nbsp;</strong>We make things rather than telling people how to make them.<br><strong>Collaboration:&amp;nbsp;</strong>We believe in bringing talented people together, winning together, and in learning from each other.</p><br> </div>

Job Description<br><br>We’re looking for an experienced IT Network Operation Support Engineer to join a high-performance operations environment supporting large-scale enterprise and service provider networks.<br><br>What You’ll Be Doing<br><br> Supporting 24x7 network operations, including on-call rotations Troubleshooting complex network issues across multi-vendor environments Managing configurations, upgrades, and incident resolution Monitoring performance, logs, and network health metrics Supporting change management, MOP validation, and service requests Collaborating across teams for design changes and operational excellence<br><br>What We’re Looking For<br><br> 7–10+ years of experience in IP/MPLS and network operations Strong expertise in routing protocols (OSPF, IS-IS, BGP) and MPLS technologies Hands-on experience with multi-vendor platforms (Cisco, Huawei, Juniper, F5, etc.) Exposure to network automation (Python, Ansible, Netconf/YANG) Proven ability to handle complex migrations and live network environments Relevant certifications (CCNP/CCIE or equivalent)<br><br>Engagement Details<br><br> Full-time role with long-term engagement (multi-year) 24x7 operational support environment Onsite presence required with flexibility for critical support

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> About the job Senior Linux Administrator – Modern Workplace / DevOps <p>We are hiring for a <strong>Qatar-based client</strong> seeking an experienced <strong>Senior Linux Administrator</strong> to support enterprise Linux and cloud-native platforms driving modern workplace and DevOps initiatives.</p><br> <p> <strong>Work Mode:</strong> Remote<br> <strong>Client Location:</strong> Qatar<br> <strong>Contract Duration:</strong> 12 Months (Extendable based on project requirements and performance)<br> <strong>Experience:</strong> 5–8+ Years</p><br> Key Skills Required: <p> Enterprise Linux Administration (RHEL, CentOS, Ubuntu, SUSE)<br> Kubernetes Administration (Production Environments)<br> Docker &amp; Containerization Technologies<br> Azure AKS &amp; Kubernetes Migration Experience<br> DevOps &amp; CI/CD Pipelines<br> Automation using Bash, Python, Ansible, and Terraform<br> HCL BigFix Patch Management<br> Backup, Disaster Recovery &amp; Security Management<br> Monitoring, Logging &amp; Observability Tools<br> Azure &amp; Google Cloud Platform Experience</p><br> Preferred Certifications: <p> RHCSA<br> CKA / CKS<br> AZ-104<br> AWS / Azure / GCP Certifications</p><br> What We're Looking For: <p> Strong hands-on Linux administration experience in enterprise environments<br> Expertise in Kubernetes and containerized platforms<br> Experience migrating workloads from Azure AKS to on-premises Kubernetes environments<br> Knowledge of DevSecOps practices and infrastructure automation<br> Strong troubleshooting, networking, and security skills</p><br> <br> <br> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> Job Summary <br> <p>The Vice President – Data Analysis will lead the execution of QNB’s enterprise-wide analytics strategy, ensuring insights-driven decision-making across all business units. This role involves overseeing the development of executive dashboards, KPI frameworks, performance analysis, and business intelligence solutions that drive measurable business value. The incumbent will manage a team of analysts and collaborate across functions to improve profitability, enhance operational efficiency, and ensure compliance with internal and regulatory standards.</p><br><p>It is ideal that the candidate should have: Advanced SQL and data modeling, Python, DWH / Lakehouse Design, ETL / ELT Pipeline Development, BI and reporting platforms, Data quality and reconciliation, Banking data domain knowledge, Workflow orchestration and automation, Databricks or similar, Data governance.</p><br> <br> <br><br> Main Responsibilities <br> <p>A. Shareholder &amp; Financial:</p><br><p>- Lead and manage a team of analysts, BI developers, and MIS experts with clear performance objectives and </p><br><p>KPIs.</p><br><p>- Define team OKRs aligned with strategic initiatives across business segments and digital transformation</p><br><p>- Define enterprise-level KPIs, measurement standards, and performance dashboards aligned with QNB’s strategic </p><br><p>and financial objectives.</p><br><p>- Lead data-driven financial modelling, profitability analysis, and scenario planning to support C-level decisions.</p><br><p>- Optimize business outcomes by integrating advanced analytics into key revenue-generating areas such as </p><br><p>lending, deposits, and fee income.</p><br><p>- Align analytics investments with organizational priorities and deliver maximum ROI from BI platforms and tools.</p><br><p>- Design and deliver cost-efficiency analysis for operational processes using benchmarking and variance analysis.</p><br><p>- Contribute to strategic initiatives such as Net Promoter Score (NPS) growth, client retention, and cross-sell </p><br><p>analytics.</p><br><p>- Monitor and analyse the impact of pricing, interest rate, and commission changes using dynamic models.</p><br><p>- Evaluate and mitigate financial risk exposure through data-supported key risk indicators (KRIs) and early-warning </p><br><p>models.</p><br><p>- Act within the limits of the powers delegated to the incumbent and delegate authority to the respective staff and </p><br><p>monitor exercise of the same.</p><br><p>- Demonstrate clear understanding of the important factors behind the bank's financial &amp; non-financial </p><br><p>performance.</p><br><p>B. Customer (Internal &amp; External):</p><br><p>- Serve as a senior advisor to business units by converting data into insights to drive customer-centric strategy.</p><br><p>- Collaborate with stakeholders across retail, corporate, international, and digital banking to deliver customized </p><br><p>performance reports.</p><br><p>- Lead strategic engagements with marketing, CRM, and product teams to optimize campaign effectiveness and </p><br><p>client segmentation.</p><br><p>- Oversee analytics initiatives that drive onboarding, retention, upselling, and loyalty programs.</p><br><p>- Support branch/channel performance tracking and digital adoption strategies through channel-level analytics.</p><br><p>- Ensure availability of real-time dashboards and alerts for business users through self-service BI environments.</p><br><p>- Facilitate quarterly business reviews with senior executives using insight-backed reporting packs.</p><br><p>- Build external partnerships with vendors and service providers for benchmark studies and advanced analytics </p><br><p>solutions.</p><br><p>- To assist customers in all their queries on Bank’s product and seek solution to their requests.</p><br><p>- Maintain activities in accordance with Service Level Agreements (SLAs) with internal departments/units to achieve </p><br><p>improvements in turn-around time.</p><br><p>- Build and maintain strong/effective relationships with related departments/units to achieve the Group’s objectives.</p><br><p>- Provide timely/accurate data to external/internal Auditors, Compliance, Financial Control and Risk when required.</p><br><p>C. Internal (Processes, Products, Regulatory):</p><br><p>- Own and govern the enterprise data analytics lifecycle from requirement gathering to insight delivery and </p><br><p>refinement.</p><br><p>- Design and deploy standardized business metric definitions and metadata repositories to support consistency </p><br><p>across departments.</p><br><p>- Lead cross-functional working groups to address analytics use cases across finance, risk, treasury, operations, </p><br><p>and compliance.</p><br><p>- Implement analytics frameworks to monitor and improve operational efficiency and cost optimization projects.</p><br><p>- Ensure regulatory-compliant data architecture and reporting workflows as per QCB, GDPR, and NDCP.</p><br><p>- Review, validate, and approve critical business models (profitability, risk scoring, churn) before deployment.</p><br><p>- Work closely with the Data Governance and Quality teams to ensure audit readiness of all analytical models and </p><br><p>outputs.</p><br><p>- Evaluate and implement workflow tools that integrate analytics seamlessly with business systems (CRM, core </p><br><p>banking, etc.).</p><br><p>- Continuous Improvement: [add if they have subordinates]</p><br><p>Set examples by leading improvement initiatives through cross-functional teams ensuring successes.</p><br><p>Identify and encourage people to adopt practices better than the industry standard. </p><br><p>Continuously encourage and recognise the importance of thinking out-of-the-box within the team. </p><br><p>Encourage, solicit and reward innovative ideas even in day-to-day issues.</p><br><p>D. Learning &amp; Knowledge:</p><br><p>- Build a learning roadmap for the analytics team, covering technical upskilling and business storytelling techniques.</p><br><p>- Champion a culture of analytical curiosity and insight-driven decision-making across business users.</p><br><p>- Establish a knowledge-sharing environment through analytics playbooks, case studies, and success metrics.</p><br><p>- Initiate cross-training programs for analysts to support multi-domain exposure and professional development.</p><br><p>- Stay current with evolving analytics trends, AI/ML tools, and cloud analytics capabilities in financial services.</p><br><p>- Mentor senior analysts on translating business problems into analytical hypotheses and data solutions.</p><br><p>- Facilitate monthly innovation forums to showcase high-impact analytics use cases across QNB.</p><br><p>- Drive a data literacy program across business units to build awareness and adoption of BI solutions.</p><br><p>- Hold meetings with staff and assess their performance and your teams overall performance on a regular basis.</p><br><p>- Take decisive action to ensure speedy resolution of unresolved grievances or conflicts within the team members.</p><br><p>- Identify development opportunities and activities for staff and facilitate/coach them to improve their effectives and </p><br><p>prepare them to assume greater responsibilities.</p><br><p>E. Legal, Regulatory, and Risk Framework Responsibilities:</p><br><p>- Ensure full adherence to QCB, NDCP, and international data protection and analytics governance requirements.</p><br><p>- Participate in internal and external audits as the analytics lead and provide all required documentation and </p><br><p>evidence.</p><br><p>- Implement review workflows for business reports involving sensitive KPIs or confidential customer information.</p><br><p>- Define and maintain audit trails, access controls, and documentation standards for all critical analytical assets.</p><br><p>- Support Regulatory and Risk teams with analytics for RCSA, KRI, internal capital adequacy, and Basel reporting.</p><br><p>- Oversee proper usage and lifecycle of business models approved for executive decision-making.</p><br><p>- Drive alignment of all analytics programs with the Bank’s Conduct Risk and Data Protection policies.</p><br><p>- Coordinate with Compliance and Internal Audit teams to investigate data breaches or unauthorized analytical </p><br><p>access.</p><br><p>- Complete all mandatory training provided by the Bank, attain, and maintain the required levels of competence.</p><br><p>- Attend mandatory (internal and external) seminars as instructed by the Bank.</p><br><p>F. Other:</p><br><p>- Ensure high standards of data protection and confidentiality to safeguard commercially sensitive information.</p><br><p>- Maintaining utmost confidentiality concerning customer and internal bank information obtained during the course </p><br><p>of business and provide such information on a need-to-know basis only to Senior Management of QNB, Audit and </p><br><p>Compliance functions, and relevant Regulators.</p><br><p>- Maintain high professional standards to uphold QNB's reputation and to strengthen its market leadership position.</p><br><p>- All other ad hoc duties/activities related to QNB that management might request from time to time.</p><br><p>- Build succession planning and capability mapping frameworks within the analytics function.</p><br><p>- Foster an inclusive, high-performance culture that encourages innovation and accountability.</p><br><p>- Oversee recruitment, onboarding, and development of top-tier analytical talent.</p><br><p>- Develop and implement governance policies for dashboard publishing, metric ownership, and release </p><br><p>management.</p><br><p>- Represent analytics in the bank’s technology or governance steering committees.</p><br><p>- Conduct regular performance reviews, identify skills gaps, and implement targeted development plans.</p><br> <br> <br><br> Education and Experience Requirements <br> <p>- University degree with specialization in Business Analytics, Data Science, Economics, Engineering, or related </p><br><p>fields.</p><br><p>- Master’s degree or MBA is highly preferred.</p><br><p>- Minimum 12 years’ experience in data analytics, with at least 8 years in senior leadership roles within the banking </p><br><p>or financial services sector.</p><br><p>- Track record of delivering enterprise-wide BI strategies and measurable business outcomes.</p><br> <br> <br> </div>

<p>We are looking for an experienced AI Engineer to join our team and help design, build, and scale intelligent systems that power real business solutions. You will work across LLMs, Computer Vision, Speech AI, and AI Infrastructure, embedding advanced AI into core business processes and client-facing products.</p><ul><li><strong>LLM Development:</strong> build chatbots, RAG systems, and automated document processing pipelines</li><li><strong>Computer Vision:</strong> Implement OCR for insurance documents, damage assessment from photos, and identity verification</li><li><strong>Speech AI:</strong> Develop transcription, voice analytics, and multilingual support solutions</li><li><strong>AI Infrastructure:</strong> Deploy and monitor AI models, integrate with cloud AI services</li><li><strong>Product Integration:</strong> Embed AI into business workflows, create AI APIs, and design user-friendly AI-powered experiences</li></ul><p><strong>Requirements</strong></p><ul><li>5+ years of experience in AI/ML engineering with a focus on production systems</li><li>Strong Python skills (advanced) + experience with FastAPI/Flask for API development</li><li>Hands-on experience with LLM tools: OpenAI API, LangChain, Hugging Face Transformers</li><li>Computer Vision expertise: OpenCV, PIL, YOLO, Detectron2 (or similar)</li><li>Deep learning frameworks: PyTorch or TensorFlow for fine-tuning models</li><li>Cloud experience: AWS / Azure / GCP AI services</li><li>Docker &amp; Kubernetes for AI application containerization and orchestration</li></ul><p><strong>Core AI/ML Competencies</strong></p><ul><li>Prompt Engineering designing effective prompts for LLMs</li><li>Fine-tuning and training custom LLMs adapting pre-trained models to business tasks</li><li>Experience RAG (Retrieval-Augmented Generation) working with vector DBs and embeddings</li><li>Computer Vision Pipelines from preprocessing to inference</li><li>Model Optimization quantization, pruning, ONNX for faster inference</li><li>Multimodal AI building solutions that combine text + images</li></ul><p><br></p><p><strong>Desired Candidate Profile</strong></p><p>5+ years of experience in AI/ML engineering with a focus on production systems</p><p>Strong Python skills (advanced) + experience with FastAPI/Flask for API development</p><p>Hands-on experience with LLM tools: OpenAI API, LangChain, Hugging Face Transformers</p><p>Computer Vision expertise: OpenCV, PIL, YOLO, Detectron2 (or similar)</p><p>Deep learning frameworks: PyTorch or TensorFlow for fine-tuning models</p><p>Cloud experience: AWS / Azure / GCP AI services</p><p>Docker &amp; Kubernetes for AI application containerization and orchestration</p><p><strong>Core AI/ML Competencies</strong></p><ul><li>Prompt Engineering designing effective prompts for LLMs</li><li>Fine-tuning and training custom LLMs adapting pre-trained models to business tasks</li><li>Experience RAG (Retrieval-Augmented Generation) working with vector DBs and embeddings</li><li>Computer Vision Pipelines from preprocessing to inference</li><li>Model Optimization quantization, pruning, ONNX for faster inference</li><li>Multimodal AI building solutions that combine text + images</li></ul>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> About the job Head of Technology &amp; AI <ul> <li>Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field.</li> <li>5+ years of experience in software engineering, AI, product engineering, or technology leadership within startups, SaaS companies, venture studios, or innovation-driven organizations.</li> <li>Proven experience building, leading, or co-developing digital products from concept through deployment.</li> <li>Strong expertise in Python and modern development frameworks.</li> <li>Experience with technologies such as FastAPI, React, Next.js, APIs, cloud infrastructure, and vector databases.</li> <li>Hands-on experience with Large Language Models (LLMs), AI agents, retrieval systems, embeddings, and workflow automation.</li> <li>Ability to balance strategic technology leadership with hands-on execution.</li> <li>Strong communication and stakeholder management skills.</li> <li>Experience collaborating with founders, cross-functional teams, and business leaders.</li> <li>Entrepreneurial mindset with the ability to solve complex problems in fast-paced environments.</li> </ul> <p><strong>Preferred Skills</strong></p><br> <ul> <li>AI/ML product development experience.</li> <li>Cloud-native architecture and deployment expertise.</li> <li>Experience building SaaS platforms and scalable digital products.</li> <li>Knowledge of MLOps, DevOps, and modern software engineering best practices.</li> <li>Experience working within startup ecosystems, venture-building environments, or innovation hubs.</li> </ul> <br> <br> </div>

Job Description<br><br> OCI Data Science Platform: Build, train, evaluate and deploy machine learning models using OCI Data Science, notebooks and Python-based ML solutions. Data Preparation & Feature Engineering: Perform data cleansing, feature engineering, dataset management and model validation activities. Machine Learning & Analytics: Develop supervised and unsupervised models including forecasting, classification, clustering and recommendation solutions. Model Lifecycle & Governance: Manage model lifecycle, versioning, approvals, governance and traceability through OCI Model Catalog or equivalent controls. MLOps & Deployment: Deploy models as scalable REST API endpoints and support CI/CD pipelines for model promotion and production deployment. Monitoring & Continuous Improvement: Monitor model performance, utilisation and drift and support retraining and optimisation activities. Operational Support: Provide L2/L3 support for deployed AI/ML solutions, troubleshooting, enhancements and knowledge base updates.<br><br>Responsibilities<br><br>Primary Skills:<br><br> OCI Data Science Python SQL Machine Learning Model Validation Statistical Analysis<br><br>Secondary/ Support Skills<br><br> OCI Dev Ops Docker Kubernetes Data Engineering concepts AI Governance Model monitoring<br><br>Tools/ Platforms<br><br> Jupyter Notebooks Tensor Flow PyTorch Scikit-learn OCI Object Storage Oracle Autonomous Database<br><br>Qualifications<br><br> Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering or a related field. Minimum 5 years of experience in data science, machine learning, advanced analytics or predictive modelling. Minimum 3 years of experience using OCI Data Science or equivalent cloud ML platforms. Experience developing, training, deploying and monitoring machine learning models in production environments. Strong understanding of MLOps, feature engineering, model governance and large-scale data analysis. OCI Data Science Professional, OCI AI Foundations Associate or OCI AI Professional certifications are preferred.<br><br>About Malomatia<br><br> ABOUT US <br><br>malomatia is a leading Qatar-based IT services and solutions provider, bringing together top Qatari and international talent to deliver innovative, end-to-end technology solutions that empower clients to achieve their strategic goals.<br><br>Our mission<br><br>Empowering Qatar’s businesses and governments to leap into the digital future with agile, knowledge-driven solutions.<br><br>Our vision<br><br>To become Qatar’s trusted knowledge partner in digital transformation, disrupting industries, shaping the future, and building a world-class tech ecosystem.<br><br>Driving change that makes a real impact<br><br>Since 2008, malomatia has been driving Qatar’s digital transformation through innovative, ISO-certified IT solutions. With expertise across key public and private sectors, we empower the nation’s vision with advanced services in cloud, cybersecurity, AI, and contact center excellence, elevating the role of technology in shaping Qatar’s sustainable future.<br><br>About The Team<br><br>Established in 2008, malomatia is a Qatari leader in IT services and digital transformation. We serve key sectors including Government, Healthcare, Education, Customs, and Transportation, delivering impactful solutions that support national development goals. Powered by a diverse team of skilled Qatari and international IT professionals, we deliver innovative, high-value digital solutions tailored to the unique needs of our clients.<br><br>Our mission is to inspire customers to thrive through digital excellence, and we envision becoming the trusted partner of choice in building a smarter society through technology and talent. We are driven by core values that define our culture and approach: ownership, integrity, empathy, teamwork, transparency, agility, excellence, trust, and innovation.<br><br>Join us in shaping the future of technology in Qatar

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building competitive-programming problems and their automatic graders (test cases + checkers) used to train advanced AI coding models.<br> You'll create problems, write both correct and deliberately-wrong solutions, and design the tough test cases that catch subtly-broken code.<br> Your work is what makes the training signal trustworthy.<br> To apply, share: your competitive-programming handle(s) + current/peak rating, any problem-setting experience, and IOI/ICPC/olympiad history.<br> What you'll do Write original algorithmic problems — clear statement, sound constraints, an intended solution; Write correct solutions in C++ and Python; Write realistic wrong solutions (common mistakes) to test against; Build and harden test cases: generators, edge cases, stress and "hacking" tests; Write checkers/interactors (testlib) for problems with more than one valid answer; Confirm the quality bar: correct solutions pass, wrong ones fail, within time limits.<br> What we look for Strong competitive programming background, shown by a public profile — a Codeforces rating (see levels below), OR an IOI / ICPC / national-olympiad record; Fluent contest C++ (STL, complexity); comfortable writing Python; Can explain why a solution is wrong and build an input that breaks it; Strong written English (C1+).<br> Levels (by Codeforces rating, or equivalent olympiad achievement): Associate — 1700–2100 (Expert / Candidate Master): easier problems; Expert — 2200–2600 (Master / Grandmaster): harder problems Senior / Reviewer — 2600+ (Int'l Grandmaster) or IOI/ICPC medalist: hardest problems + quality review.<br> Nice to have IOI / ICPC alumni and top competitive programmers especially encouraged to apply; Experience setting or testing problems for real contests (Codeforces rounds, ICPC, national olympiads, online judges); Testlib experience (checkers, validators, generators); Experience creating data for LLM code benchmarks or similar RL / evaluation datasets.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10-20 hours per week during active phases, based on project requirements.<br> This is an estimate, not a guaranteed workload, and applies only while the project is active.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation Paid per accepted task.<br> Your rate depends on the qualification tier you reach and how efficiently you complete tasks — up to the equivalent of $90/hr .<br> Because payment is per task, a faster pace raises your effective hourly rate.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building competitive-programming problems and their automatic graders (test cases + checkers) used to train advanced AI coding models.<br> You'll create problems, write both correct and deliberately-wrong solutions, and design the tough test cases that catch subtly-broken code.<br> Your work is what makes the training signal trustworthy.<br> To apply, share: your competitive-programming handle(s) + current/peak rating, any problem-setting experience, and IOI/ICPC/olympiad history.<br> What you'll do Write original algorithmic problems — clear statement, sound constraints, an intended solution; Write correct solutions in C++ and Python; Write realistic wrong solutions (common mistakes) to test against; Build and harden test cases: generators, edge cases, stress and "hacking" tests; Write checkers/interactors (testlib) for problems with more than one valid answer; Confirm the quality bar: correct solutions pass, wrong ones fail, within time limits.<br> What we look for Strong competitive programming background, shown by a public profile — a Codeforces rating (see levels below), OR an IOI / ICPC / national-olympiad record; Fluent contest C++ (STL, complexity); comfortable writing Python; Can explain why a solution is wrong and build an input that breaks it; Strong written English (C1+).<br> Levels (by Codeforces rating, or equivalent olympiad achievement): Associate — 1700–2100 (Expert / Candidate Master): easier problems; Expert — 2200–2600 (Master / Grandmaster): harder problems Senior / Reviewer — 2600+ (Int'l Grandmaster) or IOI/ICPC medalist: hardest problems + quality review.<br> Nice to have IOI / ICPC alumni and top competitive programmers especially encouraged to apply; Experience setting or testing problems for real contests (Codeforces rounds, ICPC, national olympiads, online judges); Testlib experience (checkers, validators, generators); Experience creating data for LLM code benchmarks or similar RL / evaluation datasets.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10-20 hours per week during active phases, based on project requirements.<br> This is an estimate, not a guaranteed workload, and applies only while the project is active.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation Paid per accepted task.<br> Your rate depends on the qualification tier you reach and how efficiently you complete tasks.<br> Because payment is per task, a faster pace raises your effective hourly rate.<br></span> </div>

Hello!! Greetings from Linnk Group!! Job Title: Data Engineer???? Location: Doha, Qatar???? Experience: 5+ Years???? Employment Type: Yearly Renewable Contract----???? Candidate Preference:✅ Candidates currently based in Qatar only.✅ Arabic & European nationals only.<br>Job Overview We are looking for an experienced Data Engineer to design, build, and optimize scalable data pipelines and modern data platforms supporting analytics and AI initiatives.<br>Key Responsibilities Build and maintain ETL/ELT data pipelines. Develop solutions using Azure Data Factory, Synapse, and Databricks. Manage relational, NoSQL, and vector databases. Design data models and optimize SQL queries. Ensure data quality, governance, and pipeline performance. Collaborate with cross-functional teams to support AI and analytics projects.<br>Required Skills5+ years of Data Engineering experience. Strong Python and SQL skills. Hands-on experience with Azure Data Factory, Synapse, and Databricks. Experience with relational, NoSQL, and vector databases (Pinecone, Weaviate, etc.). Knowledge of ETL/ELT, data modelling, and schema design. Preferred Skills Experience with LLM data preparation, RAG, and semantic search. Knowledge of Azure Open AI or other Gen AI platforms. Familiarity with data governance and compliance.<br>If you are interested in for the role, please do share your CV to subin.pv@linnk.com<br>KR,

<p>We are seeking a motivated Graduate Research Associate to support research on AI- and computer vision-based thermographic inspection of photovoltaic (PV) panels. The role involves developing deep learning algorithms, building thermal image mosaics, collecting and analyzing thermal datasets, and contributing to research on PV health monitoring. Applicants should have a bachelor's degree in Computer Science, Electrical Engineering, or a related eld, with strong programming skills in Python and/or C/C++. Familiarity with PyTorch, computer vision, and Linux is required. Experience with thermal imaging, sensor integration, data analysis, and research writing is desirable. The position is part-time and available immediately. To be considered for this position, Interested candidates should submit a resume and cover letter outlining their qualifications and research interests. Joining the CMU team opens the door to an array of exceptional benefits. Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits , take well-deserved breaks with ample paid time off and observed holidays , and rest easy with life and accidental death and disability insurance. Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access , and much more! For a comprehensive overview of the benefits available, explore our Benefits page . At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it s about finding the perfect fit for your professional growth and personal aspirations. Are you interested in an exciting opportunity with an exceptional organization?! Apply today! Location Doha, Qatar Job Function Researchers Position Type Staff Fixed Term (Fixed Term) Full Time/Part time Part time Pay Basis Hourly More Information: Please visit Why Carnegie Mellon to learn more about becoming part of an institution inspiring innovations that change the world. Click here to view a listing of employee benefits Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran . Statement of Assurance</p><p><strong>Desired Candidate Profile</strong></p><p>Applicants should have a bachelor's degree in Computer Science, Electrical Engineering, or a related eld, with strong programming skills in Python and/or C/C++. Familiarity with PyTorch, computer vision, and Linux is required. Experience with thermal imaging, sensor integration, data analysis, and research writing is desirable.</p>

Job Description<br><br>Must Have<br><br> 6–10 years of experience in data science, analytics, or applied statistics, including a demonstrable track record of leading projects end to end. Senior technical voice on a data science team — setting modeling standards, reviewing peer work, and mentoring less-experienced data scientists. Deep command of statistics, experimental design, and a broad modeling toolkit spanning classical machine learning, time-series, and deep learning. Demonstrated ability to translate ambiguous business and policy questions into rigorous, decision-ready analysis for executive audiences. Commitment to statistical soundness, reproducibility, and continuous learning in statistical and machine-learning methods. <br><br>Nice to have<br><br> Experience in the government or large-enterprise sector, ideally in Qatar or the wider GCC. Familiarity with Oracle Cloud Infrastructure (OCI) and cloud-based analytics environments. Exposure to deploying models into production in partnership with engineering teams. Domain expertise in a relevant vertical such as public sector, finance, telecom, or healthcare. Experience with causal inference or advanced experimentation methods. Working knowledge of data visualization or business-intelligence tools for stakeholder communication. Relevant data science or cloud certifications. <br><br>Responsibilities<br><br> Lead the design and execution of advanced analytics and statistical modeling projects, from problem framing through to validated, decision-ready insight. Translate ambiguous business and policy questions into well-defined data science problems, measurable hypotheses, and analytical plans. Define and enforce modeling methodology, experimentation standards (including A/B testing and quasi-experimental designs), and model validation practices across the team. Build, evaluate, and interpret advanced predictive and statistical models using Python (pandas, scikit-learn, statsmodels) and SQL. Select appropriate techniques across regression, classification, clustering, time-series, deep learning, and causal inference, and justify trade-offs to stakeholders. Own the statistical soundness of analytical deliverables, including assumptions, uncertainty quantification, and limitations. Establish reproducible analytical workflows and promote good practice in code quality, documentation, and version control within the team. Present findings and recommendations to senior, often non-technical, stakeholders through clear narratives and visualizations that drive decisions. Review and provide technical feedback on the analytical work of data scientists, raising the overall standard of the team. Mentor and coach junior and mid-level data scientists, supporting their technical and professional growth. Partner with machine-learning and AI engineers to hand off validated models for productionization and to define monitoring and success metrics. Contribute to proposals, scoping, and effort estimation for new data science engagements. <br><br>Qualifications<br><br> Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field; Master's or PhD preferred. Deep proficiency in Python for analysis and the scientific stack (pandas, Num Py, scikit-learn, statsmodels) and strong SQL. Strong foundation in statistics and experimental design, with command of a broad range of modeling techniques. Hands-on experience applying deep learning and neural network architectures using frameworks such as Tensor Flow or PyTorch. Experience designing and interpreting experiments and translating results into business recommendations. Demonstrated ability to frame business problems and communicate analytical results to executive and non-technical audiences. Experience mentoring analysts or data scientists and setting analytical standards or methodology. Strong understanding of the end-to-end data science lifecycle, including data quality, validation, and model handoff. Ability to manage multiple workstreams and stakeholders simultaneously.

Job Description<br><br>Must Have<br><br> 6–10 years of experience in data science, analytics, or applied statistics, including a demonstrable track record of leading projects end to end. Senior technical voice on a data science team — setting modeling standards, reviewing peer work, and mentoring less-experienced data scientists. Deep command of statistics, experimental design, and a broad modeling toolkit spanning classical machine learning, time-series, and deep learning. Demonstrated ability to translate ambiguous business and policy questions into rigorous, decision-ready analysis for executive audiences. Commitment to statistical soundness, reproducibility, and continuous learning in statistical and machine-learning methods. <br><br>Nice to have<br><br> Experience in the government or large-enterprise sector, ideally in Qatar or the wider GCC. Familiarity with Oracle Cloud Infrastructure (OCI) and cloud-based analytics environments. Exposure to deploying models into production in partnership with engineering teams. Domain expertise in a relevant vertical such as public sector, finance, telecom, or healthcare. Experience with causal inference or advanced experimentation methods. Working knowledge of data visualization or business-intelligence tools for stakeholder communication. Relevant data science or cloud certifications. <br><br>Responsibilities<br><br> Lead the design and execution of advanced analytics and statistical modeling projects, from problem framing through to validated, decision-ready insight. Translate ambiguous business and policy questions into well-defined data science problems, measurable hypotheses, and analytical plans. Define and enforce modeling methodology, experimentation standards (including A/B testing and quasi-experimental designs), and model validation practices across the team. Build, evaluate, and interpret advanced predictive and statistical models using Python (pandas, scikit-learn, statsmodels) and SQL. Select appropriate techniques across regression, classification, clustering, time-series, deep learning, and causal inference, and justify trade-offs to stakeholders. Own the statistical soundness of analytical deliverables, including assumptions, uncertainty quantification, and limitations. Establish reproducible analytical workflows and promote good practice in code quality, documentation, and version control within the team. Present findings and recommendations to senior, often non-technical, stakeholders through clear narratives and visualizations that drive decisions. Review and provide technical feedback on the analytical work of data scientists, raising the overall standard of the team. Mentor and coach junior and mid-level data scientists, supporting their technical and professional growth. Partner with machine-learning and AI engineers to hand off validated models for productionization and to define monitoring and success metrics. Contribute to proposals, scoping, and effort estimation for new data science engagements. <br><br>Qualifications<br><br> Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field; Master's or PhD preferred. Deep proficiency in Python for analysis and the scientific stack (pandas, Num Py, scikit-learn, statsmodels) and strong SQL. Strong foundation in statistics and experimental design, with command of a broad range of modeling techniques. Hands-on experience applying deep learning and neural network architectures using frameworks such as Tensor Flow or PyTorch. Experience designing and interpreting experiments and translating results into business recommendations. Demonstrated ability to frame business problems and communicate analytical results to executive and non-technical audiences. Experience mentoring analysts or data scientists and setting analytical standards or methodology. Strong understanding of the end-to-end data science lifecycle, including data quality, validation, and model handoff. Ability to manage multiple workstreams and stakeholders simultaneously.

Job Description<br><br>Must Have<br><br> 6–10 years of experience in data science, analytics, or applied statistics, including a demonstrable track record of leading projects end to end. Senior technical voice on a data science team — setting modeling standards, reviewing peer work, and mentoring less-experienced data scientists. Deep command of statistics, experimental design, and a broad modeling toolkit spanning classical machine learning, time-series, and deep learning. Demonstrated ability to translate ambiguous business and policy questions into rigorous, decision-ready analysis for executive audiences. Commitment to statistical soundness, reproducibility, and continuous learning in statistical and machine-learning methods. <br><br>Nice to have<br><br> Experience in the government or large-enterprise sector, ideally in Qatar or the wider GCC. Familiarity with Oracle Cloud Infrastructure (OCI) and cloud-based analytics environments. Exposure to deploying models into production in partnership with engineering teams. Domain expertise in a relevant vertical such as public sector, finance, telecom, or healthcare. Experience with causal inference or advanced experimentation methods. Working knowledge of data visualization or business-intelligence tools for stakeholder communication. Relevant data science or cloud certifications. <br><br>Responsibilities<br><br> Lead the design and execution of advanced analytics and statistical modeling projects, from problem framing through to validated, decision-ready insight. Translate ambiguous business and policy questions into well-defined data science problems, measurable hypotheses, and analytical plans. Define and enforce modeling methodology, experimentation standards (including A/B testing and quasi-experimental designs), and model validation practices across the team. Build, evaluate, and interpret advanced predictive and statistical models using Python (pandas, scikit-learn, statsmodels) and SQL. Select appropriate techniques across regression, classification, clustering, time-series, deep learning, and causal inference, and justify trade-offs to stakeholders. Own the statistical soundness of analytical deliverables, including assumptions, uncertainty quantification, and limitations. Establish reproducible analytical workflows and promote good practice in code quality, documentation, and version control within the team. Present findings and recommendations to senior, often non-technical, stakeholders through clear narratives and visualizations that drive decisions. Review and provide technical feedback on the analytical work of data scientists, raising the overall standard of the team. Mentor and coach junior and mid-level data scientists, supporting their technical and professional growth. Partner with machine-learning and AI engineers to hand off validated models for productionization and to define monitoring and success metrics. Contribute to proposals, scoping, and effort estimation for new data science engagements. <br><br>Qualifications<br><br> Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field; Master's or PhD preferred. Deep proficiency in Python for analysis and the scientific stack (pandas, Num Py, scikit-learn, statsmodels) and strong SQL. Strong foundation in statistics and experimental design, with command of a broad range of modeling techniques. Hands-on experience applying deep learning and neural network architectures using frameworks such as Tensor Flow or PyTorch. Experience designing and interpreting experiments and translating results into business recommendations. Demonstrated ability to frame business problems and communicate analytical results to executive and non-technical audiences. Experience mentoring analysts or data scientists and setting analytical standards or methodology. Strong understanding of the end-to-end data science lifecycle, including data quality, validation, and model handoff. Ability to manage multiple workstreams and stakeholders simultaneously.

About Commercial Bank Of Qatar<br><br>Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar’s economic development by offering a range of personal, business, government, international and investment services.<br><br>We believe in empowering our employees, providing them with opportunities for growth and professional development.<br><br>By Joining us, you’ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential.<br><br>Join us in shaping the future of banking!<br><br>Job Summary<br><br>Responsible for developing advanced machine learning models for banking use cases including pricing optimization, customer propensity modelling, and recommendation systems. This senior role requires deep expertise in statistical modelling and machine learning combined with substantial banking and financial services domain knowledge . The position focuses on translating complex business problems in areas such as Risk, Finance, Retail Banking, and Wholesale Banking into actionable ML solutions. Will leverage their understanding of banking products, regulatory requirements, and financial metrics to build models that drive measurable business value. The role requires a balance of domain expertise and technical capability, with sufficient programming skills in Python and SQL to develop and deliver working prototypes that can be transitioned to production.<br><br>Key Accountabilities<br><br>ML Model Development Design and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structures. Build propensity models for customer behavior prediction including churn, cross-sell, upsell, and product adoption. Develop recommendation systems for personalized product offerings, next-best-action, and customer engagement. Banking Domain Application Apply deep banking domain knowledge to frame business problems as ML solutions with measurable outcomes. Partner with Risk, Finance, and business units to identify high-value modeling opportunities. Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints. Analysis & Insights Conduct exploratory data analysis to identify patterns, relationships, and modeling opportunities in banking data. Translate model outputs into actionable business recommendations and insights. Develop model performance metrics aligned with business KPIs and financial outcomes. Create data visualizations and reports for stakeholder communication. Prototyping & Delivery Develop working prototypes in Python demonstrating model functionality and business value. Create clear documentation of model methodology, assumptions, limitations, and use cases. Collaborate with ML Engineers and AI Engineers to transition prototypes to production systems. Stakeholder Collaboration Partner with business stakeholders to understand requirements and validate model outputs. Present model results, methodology, and recommendations to senior management. Contribute to model governance, validation, and documentation requirements. Ensure compliance with data policies, ethical standards, and regulatory requirements.<br><br>Key Competencies<br><br>Machine Learning & Statistics Expert knowledge of supervised and unsupervised learning techniques for classification, regression, and clustering. Deep experience with pricing models, propensity modeling, and recommendation systems. Strong foundation in statistical analysis, hypothesis testing, and experimental design. Familiarity with deep learning frameworks (Tensor Flow, PyTorch) for advanced use cases. Banking Domain Expertise Comprehensive understanding of banking products (Retail or Corporate business), services, and customer lifecycle. Knowledge of Risk functions including credit risk, market risk, and operational risk frameworks. Understanding of Finance functions including P&L drivers, cost allocation, and profitability analysis. Familiarity with regulatory requirements affecting model development (IFRS 9, Basel, etc.). Technical Skills Python for data analysis and model development (pandas, scikit-learn, XGBoost, etc.). SQL – Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries). Experience with data visualization and reporting tools. Familiarity with Git (Git Hub/Git Lab) for version control. Basic understanding of Spark for large-scale data processing. Awareness of MLOps practices and model deployment concepts (MLflow, TFX). Communication & Collaboration Ability to translate complex analytical concepts into business language for non-technical stakeholders. Strong presentation skills for executive-level communication. Experience working with cross-functional teams across business and technology. Agile methodologies (Kanban, Scrum) experience.<br><br>Qualifications & Experience<br><br>Master's degree or PhD in Finance, Economics, Statistics, Mathematics, or quantitative field strongly preferred.8+ years of experience in data science or quantitative analysis roles. Minimum 5 years of experience in banking or financial services industry is mandatory. Proven track record of delivering ML models in pricing, propensity, or recommendation domains. Background in Risk, Finance, or quantitative functions within banking preferred. Experience with model validation, governance, and regulatory requirements in financial services. Professional certifications in Risk (FRM, PRM) or Finance (CFA) are a plus.<br><br>Why Commercial Bank?<br><br> Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance. An Innovation-Driven, Digital-First Environment where employees work with the latest tools and technologies to redefine banking Opportunities for Global Partnerships & International Exposure, connecting employees with global networks and perspectives. A focus on Employee Well-being & Work-Life Balance, ensuring a healthy and supportive environment for all team members Competitive Compensation & Benefits that ensure our employees are rewarded for their dedication and performance A strong Commitment to Diversity, Equity & Inclusion, fostering a culture that values every individual’s unique perspective. <br><br>At Commercial Bank, we don’t just offer careers, We shape futures by pioneering digital transformation in Qatar’s banking sector, blending digital-first approach to redefine banking through innovative solutions.<br><br>Disclaimer<br><br>We appreciate your interest in joining CBQ! Please note that only selected candidates will be contacted for further steps in the hiring process. This job posting is for informational purposes only, and CBQ reserves the right to modify, withdraw, or close it at any time without notice.

About Commercial Bank Of Qatar<br><br>Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar’s economic development by offering a range of personal, business, government, international and investment services.<br><br>We believe in empowering our employees, providing them with opportunities for growth and professional development.<br><br>By Joining us, you’ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential.<br><br>Join us in shaping the future of banking!<br><br>Job Summary<br><br>Responsible for developing advanced machine learning models for banking use cases including pricing optimization, customer propensity modelling, and recommendation systems. This senior role requires deep expertise in statistical modelling and machine learning combined with substantial banking and financial services domain knowledge . The position focuses on translating complex business problems in areas such as Risk, Finance, Retail Banking, and Wholesale Banking into actionable ML solutions. Will leverage their understanding of banking products, regulatory requirements, and financial metrics to build models that drive measurable business value. The role requires a balance of domain expertise and technical capability, with sufficient programming skills in Python and SQL to develop and deliver working prototypes that can be transitioned to production.<br><br>Key Accountabilities<br><br>ML Model Development Design and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structures. Build propensity models for customer behavior prediction including churn, cross-sell, upsell, and product adoption. Develop recommendation systems for personalized product offerings, next-best-action, and customer engagement. Banking Domain Application Apply deep banking domain knowledge to frame business problems as ML solutions with measurable outcomes. Partner with Risk, Finance, and business units to identify high-value modeling opportunities. Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints. Analysis & Insights Conduct exploratory data analysis to identify patterns, relationships, and modeling opportunities in banking data. Translate model outputs into actionable business recommendations and insights. Develop model performance metrics aligned with business KPIs and financial outcomes. Create data visualizations and reports for stakeholder communication. Prototyping & Delivery Develop working prototypes in Python demonstrating model functionality and business value. Create clear documentation of model methodology, assumptions, limitations, and use cases. Collaborate with ML Engineers and AI Engineers to transition prototypes to production systems. Stakeholder Collaboration Partner with business stakeholders to understand requirements and validate model outputs. Present model results, methodology, and recommendations to senior management. Contribute to model governance, validation, and documentation requirements. Ensure compliance with data policies, ethical standards, and regulatory requirements.<br><br>Key Competencies<br><br>Machine Learning & Statistics Expert knowledge of supervised and unsupervised learning techniques for classification, regression, and clustering. Deep experience with pricing models, propensity modeling, and recommendation systems. Strong foundation in statistical analysis, hypothesis testing, and experimental design. Familiarity with deep learning frameworks (Tensor Flow, PyTorch) for advanced use cases. Banking Domain Expertise Comprehensive understanding of banking products (Retail or Corporate business), services, and customer lifecycle. Knowledge of Risk functions including credit risk, market risk, and operational risk frameworks. Understanding of Finance functions including P&L drivers, cost allocation, and profitability analysis. Familiarity with regulatory requirements affecting model development (IFRS 9, Basel, etc.). Technical Skills Python for data analysis and model development (pandas, scikit-learn, XGBoost, etc.). SQL – Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries). Experience with data visualization and reporting tools. Familiarity with Git (Git Hub/Git Lab) for version control. Basic understanding of Spark for large-scale data processing. Awareness of MLOps practices and model deployment concepts (MLflow, TFX). Communication & Collaboration Ability to translate complex analytical concepts into business language for non-technical stakeholders. Strong presentation skills for executive-level communication. Experience working with cross-functional teams across business and technology. Agile methodologies (Kanban, Scrum) experience.<br><br>Qualifications & Experience<br><br>Master's degree or PhD in Finance, Economics, Statistics, Mathematics, or quantitative field strongly preferred.8+ years of experience in data science or quantitative analysis roles. Minimum 5 years of experience in banking or financial services industry is mandatory. Proven track record of delivering ML models in pricing, propensity, or recommendation domains. Background in Risk, Finance, or quantitative functions within banking preferred. Experience with model validation, governance, and regulatory requirements in financial services. Professional certifications in Risk (FRM, PRM) or Finance (CFA) are a plus.<br><br>Why Commercial Bank?<br><br> Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance. An Innovation-Driven, Digital-First Environment where employees work with the latest tools and technologies to redefine banking Opportunities for Global Partnerships & International Exposure, connecting employees with global networks and perspectives. A focus on Employee Well-being & Work-Life Balance, ensuring a healthy and supportive environment for all team members Competitive Compensation & Benefits that ensure our employees are rewarded for their dedication and performance A strong Commitment to Diversity, Equity & Inclusion, fostering a culture that values every individual’s unique perspective. <br><br>At Commercial Bank, we don’t just offer careers, We shape futures by pioneering digital transformation in Qatar’s banking sector, blending digital-first approach to redefine banking through innovative solutions.<br><br>Disclaimer<br><br>We appreciate your interest in joining CBQ! Please note that only selected candidates will be contacted for further steps in the hiring process. This job posting is for informational purposes only, and CBQ reserves the right to modify, withdraw, or close it at any time without notice.

About Commercial Bank Of Qatar<br><br>Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar’s economic development by offering a range of personal, business, government, international and investment services.<br><br>We believe in empowering our employees, providing them with opportunities for growth and professional development.<br><br>By Joining us, you’ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential.<br><br>Join us in shaping the future of banking!<br><br>Job Summary<br><br>Responsible for developing advanced machine learning models for banking use cases including pricing optimization, customer propensity modelling, and recommendation systems. This senior role requires deep expertise in statistical modelling and machine learning combined with substantial banking and financial services domain knowledge . The position focuses on translating complex business problems in areas such as Risk, Finance, Retail Banking, and Wholesale Banking into actionable ML solutions. Will leverage their understanding of banking products, regulatory requirements, and financial metrics to build models that drive measurable business value. The role requires a balance of domain expertise and technical capability, with sufficient programming skills in Python and SQL to develop and deliver working prototypes that can be transitioned to production.<br><br>Key Accountabilities<br><br>ML Model Development Design and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structures. Build propensity models for customer behavior prediction including churn, cross-sell, upsell, and product adoption. Develop recommendation systems for personalized product offerings, next-best-action, and customer engagement. Banking Domain Application Apply deep banking domain knowledge to frame business problems as ML solutions with measurable outcomes. Partner with Risk, Finance, and business units to identify high-value modeling opportunities. Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints. Analysis & Insights Conduct exploratory data analysis to identify patterns, relationships, and modeling opportunities in banking data. Translate model outputs into actionable business recommendations and insights. Develop model performance metrics aligned with business KPIs and financial outcomes. Create data visualizations and reports for stakeholder communication. Prototyping & Delivery Develop working prototypes in Python demonstrating model functionality and business value. Create clear documentation of model methodology, assumptions, limitations, and use cases. Collaborate with ML Engineers and AI Engineers to transition prototypes to production systems. Stakeholder Collaboration Partner with business stakeholders to understand requirements and validate model outputs. Present model results, methodology, and recommendations to senior management. Contribute to model governance, validation, and documentation requirements. Ensure compliance with data policies, ethical standards, and regulatory requirements.<br><br>Key Competencies<br><br>Machine Learning & Statistics Expert knowledge of supervised and unsupervised learning techniques for classification, regression, and clustering. Deep experience with pricing models, propensity modeling, and recommendation systems. Strong foundation in statistical analysis, hypothesis testing, and experimental design. Familiarity with deep learning frameworks (Tensor Flow, PyTorch) for advanced use cases. Banking Domain Expertise Comprehensive understanding of banking products (Retail or Corporate business), services, and customer lifecycle. Knowledge of Risk functions including credit risk, market risk, and operational risk frameworks. Understanding of Finance functions including P&L drivers, cost allocation, and profitability analysis. Familiarity with regulatory requirements affecting model development (IFRS 9, Basel, etc.). Technical Skills Python for data analysis and model development (pandas, scikit-learn, XGBoost, etc.). SQL – Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries). Experience with data visualization and reporting tools. Familiarity with Git (Git Hub/Git Lab) for version control. Basic understanding of Spark for large-scale data processing. Awareness of MLOps practices and model deployment concepts (MLflow, TFX). Communication & Collaboration Ability to translate complex analytical concepts into business language for non-technical stakeholders. Strong presentation skills for executive-level communication. Experience working with cross-functional teams across business and technology. Agile methodologies (Kanban, Scrum) experience.<br><br>Qualifications & Experience<br><br>Master's degree or PhD in Finance, Economics, Statistics, Mathematics, or quantitative field strongly preferred.8+ years of experience in data science or quantitative analysis roles. Minimum 5 years of experience in banking or financial services industry is mandatory. Proven track record of delivering ML models in pricing, propensity, or recommendation domains. Background in Risk, Finance, or quantitative functions within banking preferred. Experience with model validation, governance, and regulatory requirements in financial services. Professional certifications in Risk (FRM, PRM) or Finance (CFA) are a plus.<br><br>Why Commercial Bank?<br><br> Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance. An Innovation-Driven, Digital-First Environment where employees work with the latest tools and technologies to redefine banking Opportunities for Global Partnerships & International Exposure, connecting employees with global networks and perspectives. A focus on Employee Well-being & Work-Life Balance, ensuring a healthy and supportive environment for all team members Competitive Compensation & Benefits that ensure our employees are rewarded for their dedication and performance A strong Commitment to Diversity, Equity & Inclusion, fostering a culture that values every individual’s unique perspective. <br><br>At Commercial Bank, we don’t just offer careers, We shape futures by pioneering digital transformation in Qatar’s banking sector, blending digital-first approach to redefine banking through innovative solutions.<br><br>Disclaimer<br><br>We appreciate your interest in joining CBQ! Please note that only selected candidates will be contacted for further steps in the hiring process. This job posting is for informational purposes only, and CBQ reserves the right to modify, withdraw, or close it at any time without notice.