Director of Data Science, Credit & Fraud Risk Modeling

Kafene, New York, NY +1

The role

Get to Know Us

Kafene is revolutionizing the lease-to-own space. We're the point-of-sale powerhouse making flexible lease-to-own accessible to everyone—prime and non-prime customers alike. Our secret weapon? Cutting-edge AI and machine learning that analyzes 20,000+ data inputs in real-time, empowering retailers across furniture, appliances, electronics, tires, and durable goods to say "yes" to more customers.

The numbers tell our story: over $500 million in originations and counting. But we're just getting started.

Our 175-person team spans NYC headquarters, Wilmington, and remote talent across the globe —all united by a culture that thrives on collaboration, innovation, and genuine support. We don't just talk about great workplace culture; we deliver it. That's why Built In named us a Startup to Watch https://www.linkedin.com/company/built-in/?lipi=urn%3Ali%3Apage%3Ad_flagship3_detail_base%3B2jXPbPYzTW6P2Su6%2BfrBvw%3D%3D and Forbes recognized us as one of the Best Startup Employers https://www.forbes.com/sites/rachelpeachman/2023/03/07/meet-americas-best-startup-employers-2023/?sh=5a67d150410c.

All jobs

Director of Data Science, Credit & Fraud Risk Modeling

Kafene, New York, NY +1

The role

Get to Know Us

The numbers tell our story: over $500 million in originations and counting. But we're just getting started.

What you will do

Feature Engineering: Go beyond surface-level signals — you'll mine internal and external datasets to engineer high-signal features (DTI, PTI, payment behavior, account balance patterns) that directly improve the predictive power of production credit models. Your work here changes approval outcomes for real customers.
Model Development: Own the end-to-end development of strategic credit risk models, not just as technical exercises, but as tools that shape how Kafene decides who to approve, for how much, and at what risk. Approval amount sensitivity models, credit line optimization, and loss forecasting are yours to design and improve.
Data Preparation: Source, clean, and transform messy real-world financial data into modeling-ready datasets. You set the standard for data integrity here; this isn't a role where someone else handles the data pipeline for you.
Vendor Evaluation: Evaluate third-party data vendors and scoring products on their merits, not just their sales pitch. You'll lead cost-benefit analyses and make the call on what gets integrated into our models.
Research & Innovation: You'll have the latitude to bring in new techniques from the ML literature and apply them to real credit risk problems, not POCs that die in a notebook, but improvements that ship to production.
Model Implementation & Validation: Partner with engineering to get your models into production accurately and efficiently. You'll define what "good" looks like for model validation and push for faster, more repeatable deployment.
Monitoring & Maintenance: Stay close to how your models behave in the wild. You'll lead recalibration and redevelopment when performance drifts, because you care about outcomes, not just the initial build.
Compliance & Governance: Navigate model risk governance, regulatory requirements, and data vendor usage policies with confidence. You've done this before and know that good documentation isn't bureaucracy — it's what keeps great models in production.
Cross-Functional Partnership: Translate business questions from risk, finance, and sales into modeling problems — and explain your solutions back in plain language. The best ideas here come from people who can move between the data and the boardroom.

What you need

Education: Master's or PhD in a quantitative discipline: Statistics, Mathematics, Data Science, Econometrics, or a related field. This is a modeling-first role; a software engineering background alone won't be the right fit.
Experience: 7+ years working as a Data Scientist or ML Engineer with a specific focus on predictive modeling, ideally in credit risk, fraud detection, or financial analytics. Experience deploying models that affect real credit or lending decisions is what we're looking for.
Technical Skills:
Advanced Python for statistical modeling and ML — not primarily for application development or infrastructure engineering
Strong SQL for data extraction and feature construction
Deep expertise in ML algorithms purpose-built for structured/tabular data: gradient boosting, ensemble methods, regression models, decision trees, and AutoML frameworks
Industry Background: Prior experience in consumer lending, fintech, or financial services is highly preferred; you should already understand what DTI, PTI, and vintage analysis mean without needing context.
Governance: Hands-on experience with model risk governance frameworks and working alongside validation teams; you know the SR 11-7 world and aren't intimidated by it.
Communication: You can explain a gradient boosting model to a risk committee and a credit policy tradeoff to an engineer. Both matter here.

Why Kafene

Your models will directly determine credit outcomes for hundreds of thousands of real customers — this isn't a recommendations engine or an internal tool; it's the core of the business
You'll own the full ML lifecycle, from raw data to production, without having to fight for scope or hand off the interesting parts to someone else
ML is treated as a strategic asset at Kafene, not a support function; you'll present to executives and shape credit policy, not just fulfill tickets
Competitive compensation, remote flexibility, and a team that's genuinely excited about the problems they're solving, including the hard regulatory and data constraints that make credit risk interesting

Director of Data Science, Credit & Fraud Risk Modeling

The role

Director of Data Science, Credit & Fraud Risk Modeling

The role

What you will do

What you need

Pay and benefits

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Kafene