About the Role
We are looking for a hands-on Data Scientist with deep expertise in credit and lending analytics. You'll translate traditional financial models built by analysts into scalable, production-grade ML models, moving beyond automated platforms like DataRobot toward custom development and deployment on AWS (SageMaker, Bedrock, etc.).
Must Have Requirements
- 4–7 years of experience in financial modeling, credit risk, or lending analytics
- Expertise in Python and ML modeling (scikit-learn, XGBoost, etc.), feature engineering (WOE/IV, bureau data), validation (AUC-ROC, R², KS, backtesting), and explainability (SHAP, LIME)
- Experience deploying models on AWS (SageMaker, Bedrock, Lambda, S3)
- Proven background in credit modeling, scorecards, and PD/LGD/EAD estimation
- Familiarity with Excel-based financial models and the ability to operationalize them
- Prior experience in banks or credit-focused institutions (e.g., Credit Suisse, fintech lenders)
- Experience with US credit data and bureaus (Experian, Equifax, TransUnion)
Nice to Have
- Experience with MLOps, model governance, and explainability frameworks
- Exposure to DataRobot
- Relevant Master's / PhD degree
- Familiarity with US lending regulations (FCRA, ECOA) or IFRS 9 compliance
Key Skills & Competencies
- Strong analytical and problem-solving skills
- Ability to translate complex financial models into scalable technical implementations
- Strong programming skills in Python and machine learning frameworks
- Good understanding of financial data, lending products, and risk analytics
- Ability to communicate technical insights clearly to cross-functional stakeholders
- Comfortable working in a fast-paced, product-driven environment
Mode
- Remote / Hybrid role
- Candidates must be comfortable working evening hours in India to ensure sufficient overlap with cross-geography (e.g., US-based) teams for collaboration and meetings
