Applied Data Scientist

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.