Job Description

Roles & Responsibilities

Key Accountabilities

Machine Learning 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 machine learning solutions with measurable outcomes
• Partner with Risk, Finance, and business units to identify high-value modelling opportunities
• Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints

Analysis & Insights

• Conduct exploratory data analysis to identify patterns, relationships, and modelling 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 into production systems

Stakeholder Collaboration & Governance

• 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

Machine Learning & Statistics

• Expert knowledge of supervised and unsupervised learning techniques for classification, regression, and clustering
• Deep experience with pricing models, propensity modelling, and recommendation systems
• Strong foundation in statistical analysis, hypothesis testing, and experimental design
• Familiarity with deep learning frameworks such as TensorFlow and PyTorch

Banking Domain Expertise

• Comprehensive understanding of banking products (Retail or Corporate), 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 impacting model development (e.g., IFRS 9, Basel)

Communication & Collaboration

• Ability to translate complex analytical concepts into business language for non-technical stakeholders
• Strong executive-level presentation skills
• Experience working with cross-functional business and technology teams
• Experience with Agile methodologies (Kanban, Scrum)

Desired Candidate Profile

Requirements

• Bacheloru2019s or Masteru2019s degree in Computer Science, Software Engineering, Data Science, or a related field.
• 4u20136 years of experience in software engineering, with at least 2 years focused on AI/ML applications.
• Hands-on experience with cloud platforms (Azure or GCP) and containerization (Docker, OpenShift/K8s).
• Experience with document processing, metadata extraction, and knowledge management systems preferred.
• Banking or financial services industry experience is a plus.
• Relevant certifications (Azure AI Engineer, GCP ML Engineer) preferred.