Data Science - Credit Risk

Data Scientist – Credit Risk

About bluCognition :

bluCognition is an AI / ML based start-up specializing in risk analytics, data conversion and data enrichment capabilities. Founded in 2017, by some very named senior professionals from the financial services industry, the company is headquartered in the US, with the delivery centre based in Pune. We build all our solutions while leveraging the latest technology stack in AI, ML and NLP combined with decades of experience in risk management at some of the largest financial services firms in the world. Our clients are some of the biggest and the most progressive names in the financial services industry. We are entering a significant growth phase and are looking for individuals with entrepreneurial mindset who wants us to join us in this exciting journey.

About the Role

We are looking for analytically strong professionals with deep expertise in credit card analytics, portfolio management, and risk strategy—particularly those who have worked on LTV (Lifetime Value) and Risk–Reward trade-off modelling.

The ideal candidate brings experience working with U.S. credit card portfolios and can connect data-driven insights to business profitability and portfolio health.

Key Responsibilities

Develop, validate, and enhance LTV models, credit risk models, and portfolio profitability frameworks.

Work on Risk–Reward trade-off modelling to optimize acquisition and portfolio strategies—balancing growth and default risk.

Extract and explore data, validate data integrity, perform ad hoc analysis, evaluate new data sources for usage in strategy development.

Lead portfolio analytics initiatives to track and improve credit performance.

Partner with risk, product, and marketing teams to design data-driven credit policies, limit strategies, and customer lifecycle interventions.

Conduct profitability segmentation to identify high-value and high-risk customer cohorts.

Support pricing and credit limit optimization using predictive modelling and scenario simulations.

Analyze large-scale datasets (internal + bureau data) to extract actionable insights for portfolio health monitoring.

Required Skills & Experience

2 years + of experience in credit card, consumer lending, or portfolio risk space.

Proven experience in LTV modelling, risk–reward trade-off, and portfolio profitability optimization.

Hands-on proficiency in Python or R for statistical modelling and SQL for data extraction / manipulation.

Expertise in predictive modelling (logistic regression, decision trees, gradient boosting, survival analysis).

Experience with credit bureau data, scorecards, and portfolio forecasting techniques and familiarity with U.S. credit systems.

Ability to communicate findings clearly to both technical and business audiences.

Educational Qualifications

Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Why Join

Work on end-to-end credit portfolio analytics driving real financial outcomes.

Exposure to U.S. credit card portfolios and data-driven risk strategy.

Collaborate with cross-functional teams across risk, marketing, and product to shape growth and profitability.