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.

👻 Ghost score 21%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Kafene

WebsiteLinkedInAll Job Openings

51 - 200 employees

💸 Finance

💳 Fintech

💰 $18M Series B on 2022-09

Finance • Fintech

Kafene is a company specializing in flexible lease-to-own (LTO) programs that help merchants and consumers from all credit profiles. They offer personalized pricing by tailoring leases to each customer's risk profile and provide innovative application processes that approve customers regardless of their credit history. Kafene integrates with point-of-sale systems to facilitate seamless transactions and offers diverse payment plans to accommodate different customer budgets. Their focus on transparency, consumer education, and reporting payment history to credit bureaus aims to help consumers build better credit futures. Kafene's services lead to higher conversion rates and average order values for merchants.

📋 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

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