Analyst - Data Science

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

Job summary

bluCognition is seeking a Data Scientist focused on credit risk analytics to advance LTV and profitability models for U.S. credit portfolios. You will translate data insights into actionable strategies for risk, pricing, and growth.

The role requires hands-on modeling in Python or R, experience with credit bureau data and scorecards, and clear communication to technical and business audiences while collaborating with Risk, Product, and Marketing teams.

Qualifications

• Degree from a top-tier institution; quantitative field preferred; advanced degree preferred.
• 2+ years of experience in credit card, consumer lending, or portfolio risk space.
• Ability to communicate findings clearly to both technical and business audiences.

Responsibilities

• Develop, validate, and enhance LTV models, credit risk models, and portfolio profitability frameworks.
• Lead RiskReward trade-off modelling to optimize acquisition and portfolio strategies.
• Extract and explore data, validate data integrity, perform ad hoc analysis, evaluate new data sources.
• 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.
• Analyse large-scale datasets (internal + bureau data) to extract actionable insights for portfolio health monitoring.

Skills

LTV modelling

Risk-reward trade-off

Portfolio profitability optimization

Python or R

SQL

Predictive modelling

Credit bureau data and scorecards

Communication to technical and бизнес

Education

Quantitative degree

Tools

Python

R

SQL

Job description

Data Scientist Credit Risk

bluCognition is an AI-powered commercial credit intelligence company serving lenders and financial institutions. Our platform combines commercial credit data contributed by 150+ lenders with AI-driven analytics, banking data and fraud detection to help our clients make faster, better credit decisions. Our product suite includes bluSense, which converts bank statements into standardized, categorized transaction data with real-time financial insights, and FraudLens, which detects document tampering and first-party fraud. Alongside our data products, we run a managed services business supporting underwriting, fraud review, and KYC/KYB and sanctions screening operations for our clients, with teams across North America, Asia and Europe.

About bluCognition:

bluCognition is an AI-powered commercial credit intelligence company serving lenders and financial institutions. Our platform combines commercial credit data contributed by 150+ lenders with AI-driven analytics, banking data and fraud detection to help our clients make faster, better credit decisions. Our product suite includes bluSense, which converts bank statements into standardized, categorized transaction data with real-time financial insights, and FraudLens, which detects document tampering and first-party fraud. Alongside our data products, we run a managed services business supporting underwriting, fraud review, and KYC/KYB and sanctions screening operations for our clients, with teams across North America, Asia and Europe.

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 RiskReward 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 RiskReward 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.
• Analyse 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.