Job highlights

- Experience in credit risk modeling and scorecards with solid statistical and ML skills including XGBoost and PCA

- Develop and optimize credit risk models aligned with BASEL-II and IFRS9, estimate PD, LGD, ECL, process large datasets, integrate credit bureau data, and collaborate across teams

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.

📌 Applied Data Scientist (Delhi)
🏢 Recruito
📍 Delhi