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 recent risk-based products in days, not months.

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

📌 Data Science Lead Software Product Mumbai
🏢 KSA
📍 Mumbai