Lead Data Scientist - Fraud (Hybrid)
Posted Sep 9, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a time your fraud model's false positives or false negatives came back from an operations team and you had to rework your approach.
What they are checking: comfort with the iterative feedback loop between analytics and fraud operations that this role is built around
Tell me about a fraud pattern you identified that wasn't obvious from the data at first glance. How did you find it?
What they are checking: the inquisitive, pattern-finding instinct the posting explicitly calls out
Describe a time you had to explain a fraud model or a risk finding to a senior stakeholder who wasn't technical. How did you frame it?
What they are checking: ability to translate quantitative work into business language for non-technical leadership
Tell me about a time you managed or mentored someone on your team through a project that wasn't going well.
What they are checking: leadership substance given the role explicitly includes managing team members and coordinating their work
What fraud-specific experience do you bring from fintech or lending, and how is fraud risk different there from fraud in other industries you may have worked in?
What they are checking: depth of the required hands-on fraud experience in a lending context specifically
What would you do
Say you notice a sudden spike in a specific type of loan application that looks suspicious but doesn't match any existing fraud pattern you've modeled for. What do you do in the first 48 hours?
What they are checking: instinct for fast, structured investigation of emerging fraud before it's fully understood
If Fraud Operations pushes back that your model is flagging too many good customers and hurting approval rates, but you believe the model is catching real fraud, how do you handle that disagreement?
What they are checking: ability to hold a technical position under business pressure while still collaborating productively
Tell me about a time you had to prioritize competing requests from Fraud Operations, underwriting, and leadership all at once. How did you decide what your team worked on first?
What they are checking: ability to balance business priorities across stakeholders as both an individual contributor and a people manager
About the work
Walk me through a fraud model you built end to end, from the first hypothesis about a fraud pattern to a model running in production.
What they are checking: whether the candidate has actually owned the full lifecycle versus only building models in isolation
How do you decide where to set the threshold on a fraud score when tightening it will catch more fraud but also block more legitimate borrowers?
What they are checking: understanding of the business tradeoff between fraud loss and customer friction, not just model metrics
What's your process for pulling together and querying large transactional datasets in SQL when you're trying to scope a new fraud trend, and what do you do when the data is messy or incomplete?
What they are checking: practical fluency with large-scale, imperfect real-world data rather than textbook SQL knowledge
How have you used AI or machine learning techniques beyond traditional statistical models in a fraud or risk context, and how did you validate that it actually worked?
What they are checking: real hands-on experience applying AI in production versus buzzword familiarity
How do you monitor a fraud model after it's deployed to know when it's starting to drift or lose effectiveness as fraudsters adapt?
What they are checking: whether the candidate treats deployment as the start of ongoing ownership, not the finish line
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Practice this out loudThe full job description
As published by Enova.