Credit Risk Modeling & ML Scientist

Recruito; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

Recruito is seeking a data science professional to develop credit risk models, scorecards, and portfolio risk models in a BFSI context. You will apply supervised and unsupervised ML techniques, including XGBoost and PCA, and ensure models align with Basel II and IFRS9 standards.

The role involves estimating PD, LGD, and ECL, processing large datasets with EDA, and feature engineering. You will collaborate with business, product, and engineering teams while mentoring junior members.

Qualifications

• 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, explainability, and responsible AI.
• Research, publications, or open-source contributions are a plus.

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, explainability, and production use.
• Collaborate with business, product, and engineering teams.
• Research new ML techniques and mentor junior team members.

Skills

Credit risk modeling

ML techniques

Data analysis

XGBoost

PCA

IFRS9 awareness

Basel II knowledge

Explainability

Governance

Tools

XGBoost

PCA

Job description

Recruito is seeking a data science professional to develop credit risk models, scorecards, and portfolio risk models in a BFSI context. You will apply supervised and unsupervised ML techniques, including XGBoost and PCA, and ensure models align with Basel II and IFRS9 standards.

The role involves estimating PD, LGD, and ECL, processing large datasets with EDA, and feature engineering. You will collaborate with business, product, and engineering teams while mentoring junior members.