Data Science Lead || Software Product || Mumbai

Prerequisites

• Experience: 6+ years in Data Science, with at least 3 years specifically in Credit

Scoring, Risk Analytics, or Fraud Detection.

• Academic Background: Bachelor’s or Master’s or PhD in Statistics, Mathematics,

Computer Science, or Economics.

• Tech Mastery: Expert-level Python, SQL, and hands-on experience with XGBoost,

LightGBM, and SHAP for model explainability (or similar algorithms/tools)

• Domain Knowledge: Familiarity with the India Stack, Account Aggregator (AA)

frameworks, B2B business & credit cycles and lending product constructs.

Core Competencies

• Algorithmic Architecture: Deep expertise in building PD (Probability of Default) and

LGD (Loss Given Default) models using both traditional and alternative data.

• MLOps & Engineering: Knowledge of how to move models from a Jupyter notebook

to a production-grade API that handles real-time scoring.

• Regulatory Sensitivity: Ability to build "Glass-Box" models that comply with RBI

guidelines on transparency and bias.

• Strategic Leadership: The ability to hire and mentor a team of junior DS/DEs while

communicating risk appetite clearly to the Board and Lenders.

Success Outcomes Desired

• At 3 Months: You have audited our current data sources and established a robust

ETL pipeline for credit-relevant features.

• At 6 Months: You have deployed our enhanced Credit Scoring Model that

outperforms traditional bureau scores by at least 15% in Gini coefficient/KS or

comparable statistics.

• At 12 Months: You have built a fully automated Early Warning System (EWS) and a

Feature Store that allows our product team to launch new risk-based products in

days, not months.

Skills: credit,models,risk,credit scoring,data,data science