About the Role : We are looking for a Senior Data Scientist to support fraud strategy and analytics across a consumer lending portfolio.You will analyze fraud trends, monitor decisioning outcomes, and recommend practical changes to policies, rules, thresholds, and reporting.This is a hands-on fraud-risk analytics and strategy role, not a role primarily focused on building machine learning models.You will partner with fraud operations, product, credit/risk, data engineering, and external vendors to identify emerging risks and improving fraud controls.Key Responsibilities : - Monitor fraud performance, including losses, capture rates, false positives, approval impact, vintage trends, and segment-level KPIs.- Investigate emerging fraud patterns using application, account, behavioral, device, transaction, and third-party data.- Quantify their impact and recommend mitigations.- Translate fraud analysis and investigator findings into recommendations for decision-engine rules, thresholds, policies, and operational processes.- Monitor fraud scores and scorecards for drift or deterioration and recommend adjustments or escalation when needed.- Analyze policy backtests and champion/challenger tests to assess the impact of strategy changes on fraud outcomes, approvals, and downstream credit performance.- Evaluate third-party fraud and identity signals and recommend how they should be used in fraud decisioning.- Produce recurring fraud reports and executive-level analyses covering loss drivers, fraud typologies,



and decisioning outcomes.- Work with cross-functional teams to implement changes and improve fraud data, workflows, and monitoring.Required Qualifications : - 2 - 4 years of hands-on, qualified fraud-risk strategy or fraud analytics experience in financial services or fintech.- Experience investigating fraud patterns in application, account, or transaction data and recommending changes to fraud rules, thresholds, controls, or operational processes.- Strong SQL skills and practical Python proficiency for fraud analysis, segmentation, automation, and reporting.- Working knowledge of fraud scores or scorecards, including how to interpret performance and identify when a strategy change is needed.- Building ML models is preferred.- Understanding of fraud typologies such as identity fraud, first-party fraud, synthetic identity fraud, or third-party fraud.- Ability to explain analytical findings and recommendations to technical and non-technical stakeholders.- Bachelor's degree in a quantitative field or related discipline.Preferred Qualifications : - Experience in consumer lending, loan applications, or other high-fraud-risk credit products.- Exposure to fraud decision engines and third-party identity, device, or transaction signals.- Experience assessing fraud-rule changes through backtesting or champion/challenger analysis.- Familiarity with US consumer lending risk practices or fraud-ring analysis. (ref:hirist.tech)

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