Data Science Manager - Credit Risk

Key Responsibilities :

Model Development & Research :

- Credit Risk Modeling : Design and develop end-to-end credit risk models, including application scorecards, behavioral model development, and portfolio risk modeling.

- Portfolio Analytics & Strategy : Monitor portfolio health across vintages, segments, and products through delinquency trends, roll-rate analysis, and early-warning indicators; translate these insights into risk-based pricing, underwriting policy, and credit strategy recommendations.

- Supervised Machine Learning : Apply advanced supervised machine learning techniques alongside traditional statistical frameworks like Logistic Regression, Generalized Linear Models (GLM), and XGBoost.

- Unsupervised Learning : Utilize unsupervised learning techniques like PCA (Principal Component Analysis) for dimensionality reduction and K-means customer clustering to identify risk segments, fraud vectors, and behavioral patterns.

- Risk Metrics Estimation : Own the development of core risk parameters, including Probability of Default (PD), Loss Given Default (LGD), and Expected Credit Loss (ECL).

- Performance Benchmarking : Benchmark and continuously improve model performance using appropriate evaluation metrics and experimentation frameworks.

Data Handling & Feature Engineering :

- Large-Scale Data Processing : Work with large-scale structured and unstructured datasets to build robust data pipelines, conduct exploratory data analysis, and engineer high-quality features.

- Credit Bureau Integration : Leverage deep familiarity with Credit Bureau data to extract, preprocess, and incorporate bureau features into predictive workflows.

- Data Quality Assurance : Ensure rigorous data preprocessing, augmentation, and validation to maximize model accuracy and generalization.

Algorithm Design & Optimisation :

- Production Optimization : Collaborate with cross-functional teams to design and implement scalable algorithms optimised for production environments.

- Model Explainability : Ensure risk algorithms conform to high standards of transparency, interpretability, and statistical soundness.

Stakeholder Management :

- Business Alignment : Partner closely with product, engineering, and business teams to align AI/ML solutions with organizational goals.

- Scoping Requirements : Translate ambiguous business and portfolio problems into well-scoped data science problem statements with clear success criteria.

Mentoring & Knowledge Sharing :

- Team Guidance : Mentor junior data scientists and ML engineers, providing guidance on modelling approaches, code quality, and production readiness.

Education Qualifications :

- Bachelor's or Master's degree from a premier institution in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.

Experience :

- 7 - 9 years of relevant experience in credit risk modelling and portfolio analytics, applied data science, machine learning engineering, or risk analytics specifically within the BFSI and lending domain.

- Proven track record of developing credit risk scorecards, behavioral models, and regulatory frameworks (BASEL-2 / IFRS9 / ECL / PD / LGD).

- Demonstrated experience implementing supervised machine learning techniques and unsupervised learning techniques (e.g., PCA, K-means customer clustering).

- Strong understanding of statistical fundamentals and neural network fundamentals.

- Generative AI : Experience or familiarity with Agentic AI frameworks and Retrieval-Augmented Generation (RAG) architectures.

- Deep Learning : Hands-on experience applying deep learning techniques to financial services or credit risk use cases.

- Advanced LLM Frameworks : Familiarity with prompt engineering, RLHF, and LLM evaluation frameworks.

- Governance : Contributions to open-source ML projects, published research, or active participation in responsible AI and strict model governance practices within regulated industries.

Skills

Data Science, Credit Risk Modeling, Machine Learning, Generative AI, Deep Learning, Predictive Analytics, Artificial Intelligence, Data Scientist, LLM