Role Summary
Data Scientist supporting fraud strategy development, fraud analytics, and operational monitoring
across a consumer lending portfolio. Working under the fraud strategy lead and alongside senior team
members, you will analyze fraud trends, monitor portfolio and decisioning performance, and help
translate findings into policy and rule recommendations for the decision engine.

This is an individual-contributor role for someone with strong analytical fundamentals and hands-on SQL
and Python skills who wants to build depth in fraud risk. Day-to-day work is weighted toward analysis,
recurring reporting, monitoring, and cross-functional execution with fraud operations, product,
credit/risk, and data engineering — including scorecard and model performance monitoring.

Key Responsibilities
• Analyze fraud trends and emerging patterns across application, behavioral, device, and third
party data, and summarize findings with explicit supporting evidence
• Monitor fraud performance metrics and operational outcomes — loss rates, capture rates, false
positive rates, approval impact,



vintage trends, and segment-level KPIs — and flag issues for
review
• Support policy, rule, and threshold recommendations for the decision engine, sizing expected
impact and recommending changes with guidance from the fraud strategy lead and senior team
members
• Prepare recurring fraud reports and deep dives — loss attribution, typology trends, and
decisioning outcomes — with commentary on drivers of change
• Track fraud scorecard and model performance (PSI, score drift, KS, decay) and monitor reason
code and segment-level behavior, escalating signs of degradation
• Evaluate third-party fraud and identity signals (identity verification, device intelligence,
consortium data, bank/transaction data) and contribute benchmarking analysis to onboarding,
retirement, and reweighting decisions
• Support test-and-learn analyses — champion/challenger tests, policy backtests, and holdouts —
including da

📌 Data Scientist-Fraud Risk Mitigation (India)
🏢 Applied Data Finance
📍 India