Manager, Data Science – Credit & Fraud Risk Modeling

🕒 August 22

🗽 New York – Remote

🗽 New York – Remote

💵 $95k - $140k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

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Kafene

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2019

💼 Consulting

📦 Logistics

💳 Fintech

💰 $15M Debt Financing - Kafene on 2024-01

Consulting • Logistics • Fintech

Kafene is a consumer finance company that offers flexible lease-to-own and point-of-sale financing solutions for merchants and customers, especially those with limited or no credit history. It uses a data-driven underwriting approach (over 20,000 data points) and personalized pricing to approve customers quickly—often with a 60-second application—and funds leases up to $5,000. Kafene partners with retailers to increase conversion, average order value, and access customers across all credit profiles through seamless POS integrations and customizable merchant programs.

📋 Description

• Own the full lifecycle of machine learning models powering credit risk decisions • Design, build, deploy, and monitor models determining customer approvals, credit amounts, default predictions, and loss forecasts • Mine internal and external datasets to engineer high-signal features such as DTI, PTI, payment behavior, and account balance patterns • Develop strategic credit risk models, including approval amount sensitivity, credit line optimization, and loss forecasting models • Source, clean, and transform financial data into modeling-ready datasets • Evaluate third-party data vendors and scoring products; lead cost-benefit analyses and determine integrations • Apply new machine learning techniques from research to production credit risk problems • Partner with engineering on model implementation, validation, and repeatable deployment • Lead model recalibration and redevelopment when performance drifts • Navigate model risk governance, regulatory requirements, and data vendor usage policies • Translate business questions from risk, finance, and sales into modeling problems and explain solutions in plain language • Present to executives and shape credit policy

🎯 Requirements

• Master's or PhD in a quantitative discipline: Statistics, Mathematics, Data Science, Econometrics, or a related field • 5+ years working as a Data Scientist or ML Engineer with a specific focus on predictive modeling • Experience ideally in credit risk, fraud detection, or financial analytics • Experience deploying models affecting real credit or lending decisions • Advanced Python for statistical modeling and ML • Strong SQL for data extraction and feature construction • Deep expertise in structured/tabular-data ML algorithms: gradient boosting, ensemble methods, regression models, decision trees, and AutoML frameworks • Prior experience in consumer lending, fintech, or financial services is highly preferred • Hands-on experience with model risk governance frameworks and working alongside validation teams • Familiarity with SR 11-7 • Ability to explain technical models to risk committees and credit policy tradeoffs to engineers • Hands-on credit risk modeling experience

🏖️ Benefits

• 80% coverage of medical, dental, and vision insurance costs, including coverage for spouse, children, and other dependents • 401k plan • Flexible paid time off days starting from day one • Remote flexibility • Competitive compensation