Senior Data Scientist - Credit Risk

Senior Data Scientist - Credit Risk & Provisioning Models Engineering · Stockholm · Full-time · kr 611,050 SEK - kr 843,151 SEK Overview Application What You'll Do Develop and maintain credit risk models for Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and lifetime Expected Credit Loss (ECL) across multiple regions and products. Train gradient boosting models (LightGBM, XGBoost) for credit risk prediction with rigorous calibration, backtesting, and out-of-time validation. Design and implement vectorized models for computing forward-looking lifetime ECL estimates, incorporating macroeconomic scenarios and discounting. Perform feature engineering on credit datasets including payment behavior, delinquency patterns, bureau credit scores, and transactional features. Manage the full model lifecycle using MLflow for experiment tracking, model versioning, and registry, ensuring reproducibility and complete audit trails. Build and maintain model monitoring to track performance, stability, and drift across markets, producing dashboards and automated alerts. Develop macro-overlay models that incorporate macroeconomic variables (unemployment, GDP, interest rates) into forward-looking credit loss projections. Support fair value estimation and coverage rate analysis for debt sale pricing and capital management decisions. Run end-of-month production scoring - loading trained models, scoring exposure data at scale on cloud compute, and validating ECL outputs. Maintain model documentation and support audit reviews, regulatory inquiries, and model validation exercises. Collaborate with Data Engineers to define feature requirements, validate pipeline outputs, and ensure model inputs are accurate and timely. Present results to senior stakeholders including Finance leadership, auditors, and regulatory reviewers. Who you are 3+ years of experience in a Data Science, Quantitative Analyst, or Credit Risk Modeling role. Strong Python skills for modeling, analysis, and production code (pandas, NumPy, scikit-learn). Experience with gradient boosting frameworks - LightGBM, XGBoost, or CatBoost. Solid statistical foundations - probability theory, hypothesis testing, regression, time series, survival analysis, or transition matrices. SQL proficiency - complex analytical queries on a data warehouse for feature extraction, validation, and ad-hoc analysis. Model lifecycle experience - training, hyperparameter tuning, validation, deployment, and monitoring. Experience with experiment tracking tools such as MLflow, Weights & Biases, or similar. Strong communication skills - ability to explain model behavior, limitations, and results to non-technical stakeholders. Awesome to have Credit risk modeling experience - PD, LGD, EAD, transition matrices, vintage analysis, or roll-rate models. IFRS 9 / CECL knowledge - staging criteria, lifetime vs. 12-month ECL, forward-looking adjustments, macroeconomic overlays. Familiarity with model interpretability techniques (SHAP, feature importance, partial dependence plots). Experience with Bayesian optimization for hyperparameter tuning. Exposure to Numba or vectorized computation for high-performance model calculations. Familiarity with fair value or pricing models for consumer credit portfolios. Understanding of cloud infrastructure (AWS S3, Batch, Docker) for model deployment and scoring. Background in fintech, banking, or consumer lending. Apply Hire with Privacy Policy