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Lead Data Scientist - Fraud (Hybrid)

Enova · Chicago, IL · Analytics

Posted Sep 9, 2026 · We last checked this listing on Sep 20, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

Behavioral

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.

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?

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?

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.

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?

depth of the required hands-on fraud experience in a lending context specifically

Technical

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.

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?

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?

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?

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?

whether the candidate treats deployment as the start of ongoing ownership, not the finish line

Situational

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?

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?

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?

ability to balance business priorities across stakeholders as both an individual contributor and a people manager

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The full job description

As published by Enova.

We are interested in every qualified candidate who is eligible to work in the United States. However, we are not able to sponsor visas or take over sponsorship at this time. About the role: Staying a step ahead of fraudsters takes an inquisitive mind, an appetite to dig deeper, and the imagination to shed new light on how we fight fraud — and here, it all starts with data. As a Lead Data Scientist on Enova's Fraud Analytics team, you'll be the quantitative engine of our fraud prevention effort. You'll develop, enhance, and test the models and pattern-recognition pipelines that surface emerging fraud trends across our lending products — then work hand-in-hand with our Fraud Operations team, who investigate the individual applications your models flag. Their findings (the false positives and false negatives) come back to you to sharpen the identifying characteristics and pivot the approach. It's a fast, iterative loop, and you sit at the center of it. The broader Enova Analytics department consists of 100 quantitative professionals dedicated to using the latest cutting-edge techniques to drive business value: providing customers with access to fast, trustworthy credit while managing risk. Our company-wide, data-driven culture means you spend less time presenting and more time on the fun part: crunching data. Key responsibilities: <ul> <li style="font-family: helvetica, arial, sans-serif;">Develop, deploy, and monitor models and pattern-recognition algorithms to detect emerging and shifting fraud trends across one or more lending products</li> <li style="font-family: helvetica, arial, sans-serif;">Write customized programs in Python for meaningful data analysis and predictive modeling, and query large, complex datasets in SQL</li> <li style="font-family: helvetica, arial, sans-serif;">Partner closely with Fraud Operations through the full detection loop — pulling data together, surfacing suspicious patterns, and incorporating their investigation results to refine features and reduce false positives/negatives</li> <li style="font-family: helvetica, arial, sans-serif;">Conduct ad hoc analysis on large, complex datasets to scope new or changing fraud trends and recommend risk, verification, and operational strategies</li> <li style="font-family: helvetica, arial, sans-serif;">Communicate findings clearly to cross-functional partners, provide requirements, and support implementation</li> <li style="font-family: helvetica, arial, sans-serif;">Help improve underwriting and verification processes from a fraud-risk perspective</li> <li style="font-family: helvetica, arial, sans-serif;">Apply AI in production applications to streamline fraud prevention processes</li> <li style="font-family: helvetica, arial, sans-serif;">Manage team members, and help coordinate their work with business priorities. </li> </ul> Requirements: <ul> <li style="font-family: helvetica, arial, sans-serif;">5+ years of experience in analytics, applied machine learning, or quantitative modeling</li> <li style="font-family: helvetica, arial, sans-serif;">Hands-on fraud experience required — fraud analytics, fraud strategy, or risk modeling, ideally in fintech or lending</li> <li style="font-family: helvetica, arial, sans-serif;">Advanced Python and SQL; experience owning models end-to-end — design through deployment and monitoring — on large-scale transactional data</li> <li style="font-family: helvetica, arial, sans-serif;">Track record of translating analysis into business strategy and communicating with senior stakeholders</li> <li style="font-family: helvetica, arial, sans-serif;">Previous leadership experience mentoring teammates, driving team initiatives, and shaping priorities.</li> </ul> Compensation: The budgeted annual salary range for this position is $106,000 to $140,000. Actual annual salary will be determined based on qualifications, skills, experience, and level assessed during the hiring process and may fall outside of the range shown. Additional compensation for this role may include a bonus. All full-time employees are eligible to participate in Company benefits, described in more detail <a href="https://www.enova.com/culture/#:~:text=Full%2Dtime%20employees%20receive%20medical,and%20dependent%20daycare%3B%20and%20more!">here</a>.  <div class="content-conclusion">Benefits & Perks: <ul> <li>Our hybrid roles require in-office work Tuesday through Thursday, with remote flexibility on Mondays and Fridays. This schedule fosters collaboration, team connection, and strategic planning, enhancing communication and effectiveness to drive results.</li> <li style="font-weight: 400;">Health, dental, and vision insurance including mental health benefits</li> <li style="font-weight: 400;">401(k) matching plus a roth option (U.S. Based employees only)</li> <li style="font-weight: 400;">PTO & paid holidays off</li> <li style="font-weight: 400;">Sabbatical program (for eligible roles)</li> <li style="font-weight: 400;">Summer hours (for eligible roles)</li> <li style="font-weight: 400;">Paid parental leave</li> <li style="font-weight: 400;">DEI groups (B.L.A.C.K. @ Enova, HOLA @ Enova, Women @ Enova, Pride @ Enova, South Asians @ Enova, APEX @ Enova, and Parents @ Enova)</li> <li style="font-weight: 400;">Employee recognition and rewards program</li> <li style="font-weight: 400;">Charitable matching and a paid volunteer day…Plus so much more!</li> </ul> About Enova Enova International is a leading financial technology company that provides online financial services through our AI and machine learning-powered Colossus™platform. We serve non-prime consumers and businesses alike, while offering world-class technology and services to traditional banks—in order to create accessible credit for millions.  Being a values-driven organization is at the core of Enova’s success. We live our values by listening to our customers, challenging assumptions, thinking big, setting high expectations, and hiring and developing the best. Through our values and our commitment to making Enova an awesome place to work, we maintain an environment of inclusion and culture where our employees can thrive. You can learn more about Enova’s values and culture <a href="http://www.enova.com/culture">here</a>.  It is our policy to provide equal employment opportunity for all persons and not discriminate in employment decisions by placing the most qualified person in each job, without regard to any other classification protected by federal, state, or local law. California Applicants: Click<a href="https://www.enova.com/ccpa/"> here</a> to review our California Privacy Policy for Job Applicants.</div>

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Lead Data Scientist - Fraud (Hybrid)

Likely interview questions for this role

Practice this interview out loud.

The full job description

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