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 current 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.
Solid 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.

📌 Applied Data Scientist Delhi (India)
🏢 Recruito
📍 India