Job Description

Role Summary

Senior Data Scientist focused on fraud strategy analytics and operational monitoring across a consumer lending portfolio. You will turn fraud data, scorecard performance, and decisioning outcomes into actionable policy, rule, and reporting recommendations — partnering closely with fraud operations, product, credit/risk, data engineering, and external vendors. Day-to-day responsibilities include monitoring, trend detection, third-party signal assessment, and cross-functional execution.

Key Responsibilities

• Translate fraud data and model outputs into clear policy, rule, and threshold recommendations for the decision engine, and partnering with cross-functional teams to prioritize and implement them.
• Monitor portfolio fraud performance — loss rates, capture rates, false-positive rates, approval impact, vintage trends, and segment-level KPIs — and surface issues with proposed actions.
• Track scorecard and model performance (PSI, score drift, KS, decay) and recommend recalibration, rule adjustments, or escalation when performance degrades.
• Detect emerging fraud trends, rings, and cross-channel vulnerabilities through analytics on application, behavioral, device, and third-party data; size the impact and propose mitigations.
• Assess and benchmark third-party fraud and identity signals (identity verification, device intelligence, consortium data, bank/transaction data); recommend which to onboard, retire, or reweight.
• Partner with fraud operations to monitor real-time fraud trends, interpret investigator findings, and convert case-level insights into rule, policy, and reporting changes.
• Design and analyze champion/challenger tests and policy backtests to quantify the impact of strategy changes on fraud rates, approvals, and downstream credit performance.
• Produce regular fraud reporting and executive deep dives — loss attribution, typology trends, decisioning outcomes — for senior leadership.
• Collaborate with product, data engineering, credit/risk, and external vendors to evolve fraud data sources, decisioning workflows, and monitoring infrastructure.
• Act as a subject matter expert on fraud data, scorecard behavior, and decision engine outcomes for cross-functional partners.

Qualifications

• 4–7 years in fraud strategy and analytics in financial services or fintech, with a hands-on analytical focus.
• Strong understanding of fraud typologies in consumer lending — identity, synthetic, first-party, and third-party fraud — and how they manifest in application and account data.
• Working knowledge of fraud models and scorecards: how they are built, evaluated, and monitored, with the ability to interpret outputs and recommend strategy changes.
• Advanced SQL and Python proficiency for portfolio analytics, segmentation, and reporting.
• Experience working with third-party fraud data providers and integrating fraud rules or signals into decision engines.
• Clear written and verbal communication; able to translate analytics into recommendations for technical and non-technical stakeholders.
• Bachelor’s degree in a quantitative field (Statistics, Economics, Mathematics, Computer Science, Engineering, or related).

Preferred Qualifications

• Experience in consumer lending or other high-fraud-risk credit products.
• Familiarity with US consumer lending regulations and risk management practices.
• Exposure to graph or network analysis for fraud ring detection.