Applied Data Scientist - Credit Risk

The candidate will have responsibilities across the following functions:

Model Development and Research:

• Credit Risk Modelling: Design and develop end-to-end credit risk models, including application scorecards, behavioural model development, and portfolio risk modelling.
• Supervised Machine Learning: Apply advanced supervised machine learning techniques alongside traditional statistical frameworks like Logistic Regression, Generalised Linear Models (GLM), and XGBoost.
• Unsupervised Learning: Utilise unsupervised learning techniques like PCA (Principal Component Analysis) for dimensionality reduction and K-means customer clustering to identify risk segments, fraud vectors, and behavioural patterns.
• Regulatory Compliance: Develop and implement regulatory risk modelling solutions aligned with international standards such as BASEL-2 and IFRS9 frameworks. 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 and 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 maximise model accuracy and generalization.

Algorithm Design and Optimisation.

• Production Optimisation: 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.

Cross-Team Collaboration:

• Business Alignment: Partner closely with product, engineering, and business teams to align AI/ML solutions with organisational goals.
• Scoping Requirements: Translate ambiguous business and portfolio problems into well-scoped data science problem statements with clear success criteria.

Research and Innovation:

• Continuous Learning: Stay current with state-of-the-art advancements in machine learning.
• Methodology Adoption: Evaluate and adopt relevant new techniques into the team's workflow.
• Knowledge Sharing: Contribute to internal knowledge sharing and, where applicable, to external publications, technical blogs, or patents. Mentoring & Knowledge Sharing
• Team Guidance: Mentor junior data scientists and ML engineers, providing guidance on modelling approaches, code quality, and production readiness. Education

Requirements:

• Bachelor's or Master's degree from a premier institution in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• Experience
• 4-7 years of professional experience in applied data science, machine learning engineering, or risk analytics, specifically within the BFSI and lending domain.
• Proven track record of developing credit risk scorecards, behavioural 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.