Data Scientist - Credit

Duties & Responsibilities - Strengthen Credit Risk Modeling and Credit Predictability

• Build and enhance credit risk models that improve credit predictability and portfolio performance.
• Conduct exploratory data analysis and experiment tracking to identify key risk drivers and optimize model outcomes.
• Partner closely with Credit Strategy, Implementation, and Feature Platform teams to connect model insights directly to decision flows.
• Keep our modeling framework adaptive to changing credit dynamics and economic conditions.

Duties & Responsibilities - Bridge Data Science and MLOps for Reliable Model Delivery

• Own Python project structure, CI/CD setup, and end-to-end testing for reliable model delivery.
• Collaborate with MLOps and Feature Platform teams to maintain model pipelines (e.g., Metaflow, SageMaker, or equivalent orchestration frameworks).
• Support model deployment, validation, and performance consistency in production.
• Drive standardization, automation, and reproducibility across the modeling lifecycle.

Duties & Responsibilities - Support Model Monitoring and Governance

• Expand and maintain monitoring frameworks for production models.
• Support compliance reviews, validation exercises, and performance monitoring (e.g., PSI/CSI analysis, reject inference).
• Maintain audit-ready documentation and ensure transparency across model development and production processes.

Requirements

• Bachelor’s degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, Data Science, or related).
• Master’s or PhD preferred.
• Strong understanding of the model lifecycle (EDA, modeling, evaluation, deployment).
• Proficiency in Python (pandas, scikit-learn, etc.) and SQL.
• Experience with Metaflow or similar orchestration frameworks (e.g., Flyte, ZenML, SageMaker Pipelines).
• Familiarity with cloud environments (AWS preferred).
• Experience with model deployment (FastAPI + Docker or similar).
• Understanding of version control (Git/GitHub), unit testing (pytest), and modern Python tooling (e.g., uv, poetry).
• Proven accountability, communication, and work ethic — able to deliver high-quality work on time with minimal oversight.

Nice to Have

• Background in credit risk modeling or lending-related ML applications (e.g., underwriting, loss prediction, fraud detection).
• Experience integrating ML solutions with decision systems or APIs.
• Familiarity with model monitoring frameworks or model governance processes.
• Comfort working in cross-functional teams and presenting technical results to non-technical stakeholders.