Data Science

Overall, Job Description

• Own and define the credit risk strategy and risk appetite framework across retail lending portfolios ensuring alignment with business objectives and long-term risk tolerance.
• Oversee the end-to-end model lifecycle for Application, Behavioral, Collections, and IFRS 9/ECL models, including development, governance, validation, implementation, and ongoing performance optimization.
• Drive portfolio analytics and performance management through continuous monitoring of acquisition quality, vintages, delinquencies, losses, credit costs, early warning indicators, concentration risks, and emerging portfolio trends.
• Recommend enhancements to credit policies, underwriting frameworks, and decision strategies by translating analytical insights into actionable risk controls, customer segmentation, approval criteria, exposure management, and portfolio optimization initiatives.
• Build and enhance automated decisioning and Straight-Through Processing (STP) frameworks, leveraging bureau data, open banking data, alternative data sources, and advanced analytics to improve risk-adjusted growth and customer experience.
• Establish robust model risk management practices, including model validation, performance monitoring, drift detection, periodic recalibration, redevelopment, documentation, governance, and audit readiness.
• Partners with Business, Product, Technology, Data Engineering, and Operations teams to evaluate growth opportunities, implement risk strategy changes, optimize customer acquisition, and enhance portfolio profitability.
• Present portfolio insights, model outcomes, stress-testing results, and strategic recommendations to senior leadership, management committees, board-level forums, and risk governance committees.
• Ensure compliance with regulatory, governance, and audit requirements, including RBI guidelines, model governance standards, IFRS 9 requirements, internal audits, and external regulatory reviews.
• Drive risk transformation and innovation initiatives by improving data infrastructure, analytical capabilities, decision engines, monitoring frameworks, and end-to-end credit lifecycle processes.
• Lead, mentor, and develop high-performing analytics and risk teams, fostering technical excellence in statistics, machine learning, risk modelling, portfolio analytics, and strategic decision-making.

EDUCATION

Essential requirements:

- Bachelor's degree in Statistics, Mathematics, Economics, Engineering, or quantitative Finance or Management

Preferred:

Master's degree from Statistics, Mathematics, Economics, Engineering, or quantitative Finance or Management

Essential requirements:

- 10+ years of progressive experience in data science, Credit risk analytics, with a clear track record of owning portfolio risk in a bank, NBFC, or fintech.

Preferred:

Same as above