Applied Data Scientist

Recruito; Dadri, Gurugram District; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

Recruito in Dadri, India is seeking a data science professional to develop and implement credit risk models and scorecards. The role focuses on PD, LGD, and ECL within Basel II and IFRS9 frameworks, while leveraging ML methods to enhance risk insights and portfolio governance.

You will work with cross-functional teams to process large datasets, engineer bureau features, and mentor junior members as part of a scalable risk analytics platform.

Qualifications

• Experience in Data Science, ML, or Risk Analytics.
• Strong experience in credit risk modeling, scorecards, Basel II, IFRS9, PD, LGD, and ECL.
• Proficiency in ML, statistical modeling, PCA, K-means, and XGBoost.
• Experience with deep learning, GenAI, RAG, and agentic AI frameworks.

Responsibilities

• Develop credit risk models, scorecards, and portfolio risk models.
• Apply statistical, supervised, and unsupervised ML techniques.
• Build PD, LGD, and ECL models aligned with Basel II and IFRS9.
• Process large datasets and engineer credit bureau features.
• Optimize models for production with strong explainability and governance.
• Collaborate with business, product, and engineering teams.
• Research new ML/AI techniques and mentor junior team members.

Skills

Credit risk modeling

Scorecards

Basel II

IFRS9

PD/LGD/ECL

ML techniques

PCA

K-means

XGBoost

Prompt engineering

Tools

Python

R

SQL

Pandas

Job description

Responsibilities

• Develop credit risk models, scorecards, and portfolio risk models.
• Apply statistical, supervised, and unsupervised ML techniques.
• Build PD, LGD, and ECL models aligned with Basel II and IFRS9.
• Process large datasets and engineer credit bureau features.
• Optimize models for production with strong explainability and governance.
• Collaborate with business, product, and engineering teams.
• Research new ML/AI techniques and mentor junior team members.

Requirements

• Experience in Data Science, ML, or Risk Analytics.
• Strong experience in credit risk modeling, scorecards, Basel II, IFRS9, PD, LGD, and ECL.
• Proficiency in ML, statistical modeling, PCA, K-means, and XGBoost.
• Experience with deep learning, GenAI, RAG, and agentic AI frameworks.
• Knowledge of prompt engineering, RLHF, and LLM evaluation.
• Strong understanding of model governance, explainability, and responsible AI.