Applied Data Scientist

Responsibilities

• Develop credit risk models, scorecards, and portfolio risk models.
• Apply supervised and unsupervised ML techniques, including XGBoost, PCA.
• Build risk models aligned with BASEL-II and IFRS9 standards.
• Estimate PD, LGD, and Expected Credit Loss (ECL).
• Process large datasets, perform EDA, and engineer features.
• Integrate credit bureau data and ensure data quality.
• Optimize models for accuracy, explain ability, and production use.
• Collaborate with business, product, and engineering teams.
• Research new ML techniques and mentor junior team members.

Requirements

• Experience in Data Science, ML, or Risk Analytics within BFSI/lending.
• Experience developing credit risk models and scorecards.
• Strong skills in statistical modeling and supervised/unsupervised ML.
• Knowledge of deep learning and financial risk applications.
• Familiarity with agentic AI, RAG, prompt engineering, and LLM evaluation.
• Understanding of model governance, explain ability, and responsible AI.
• Research, publications, or open-source contributions are a plus.