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

Work arrangement
No