Sr. Data Scientist - Credit Risk Modeling

Senior Data Scientist Model Development (Credit Risk)

Location: Wilmington, DE - Hybrid - 3 days a week

About the Role

We are seeking a highly skilled Senior Data Scientist to lead the development, validation, and optimization of machine learning models that power credit risk decisioning across the lending lifecycle. You will work with hard-core modelers, product, and risk teams , bringing deep technical expertise and the ability to confidently address client questions. This is a hands-on role requiring strong modeling, coding, and stakeholder engagement skills.

Key Responsibilities

• Lead end-to-end credit risk model development (PD, EAD, LGD, underwriting, customer management, collections).
• Build ML models for banking products such as personal loans, home loans, credit cards, and other retail products .
• Apply and optimize ML techniques ( XGBoost, LightGBM, Gradient Boosting, Ensembles ) for accuracy, stability, and fairness.
• Conduct feature engineering, EDA, and robust model evaluation (AUC, KS, PR, Lift).
• Implement champion challenger frameworks, monitoring, calibration, and drift detection.
• Translate complex business needs into clear analytical solutions, producing well-documented methodologies and model reports.
• Collaborate with internal/external stakeholders, present insights, and defend modeling decisions with confidence.
• Stay current with emerging trends in ML, model governance, and credit risk regulations.

Required Qualifications

• 7 8+ years overall experience in analytics/modeling within Banking or Financial Institutions .
• 4 5+ years hands-on experience in machine learning model development .
• Proven track record in developing credit risk models (PD/EAD/LGD, underwriting, collections, loan/customer management).
• Strong programming in Python (pandas, scikit-learn, NumPy, ML libraries) and proficiency in SQL .
• Direct experience applying ML techniques such as XGBoost, LightGBM , and related algorithms.
• Excellent communication skills and stakeholder management experience .

Nice to Have

• Exposure to distributed data processing (Spark/Databricks).
• Experience with MLOps tools (MLflow, DVC, model deployment pipelines).
• Familiarity with regulatory/compliance needs (model risk management, bias/fair-lending, explainability using SHAP/LIME).

Why Join Us

• Work on impactful, high-visibility credit risk models shaping lending strategy.
• Collaborate with top-tier modelers and cross-functional teams.
• Opportunity to influence model development standards, governance, and innovation in BFSI analytics.