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Lead Data Scientist (Hybrid)

Enova · Chicago, IL · Analytics

Posted Sep 2, 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

Walk me through a credit risk model you validated independently. What did you check first, and what did you find?

whether the candidate has a real, structured validation process versus a surface-level review

Tell me about a time you had to challenge a model owner or business partner on their assumptions. How did that conversation go?

ability to provide effective challenge without damaging working relationships

Describe a validation report you've written. Who read it, and how did you tailor it for that audience?

ability to write documentation that satisfies both technical reviewers and non-technical stakeholders

Tell me about a time you automated part of a model validation or monitoring process. What tool did you build and what problem did it solve?

initiative in improving governance processes rather than just executing them manually

Tell me about a validation project that was more complex or contentious than expected. What made it hard, and how did you lead it to completion?

ability to lead complex analytical projects end to end, as the posting specifically calls for

Technical

How do you evaluate whether a model is conceptually sound, separate from whether it performs well on backtesting?

depth of understanding of model theory versus just checking output metrics

What statistical methods do you rely on most for assessing model fit in credit risk, and when would you choose one over another?

breadth and correctness of statistical toolkit for this specific domain

Can you write a SQL query to pull the last twelve months of loan performance data joined against application-level attributes, and then walk me through how you'd start exploring it in Python?

hands-on fluency with SQL and Python on realistic data pulls, not just theoretical knowledge

How would you explain the concept of a model's discriminatory power, like a KS statistic or AUC, to a non-technical stakeholder who's worried about a specific loan decision?

ability to translate quantitative concepts for non-technical audiences, a named requirement

What's your experience with regulatory expectations around model risk management, like SR 11-7 or similar guidance, and how has that shaped how you validate models?

familiarity with the regulatory backbone of credit model governance in financial services

Situational

Say you're monitoring a production credit model and you see a slow drift in performance on a dashboard, nothing dramatic yet. What do you do?

judgment about when to escalate versus wait, and whether they understand model risk in a live lending environment

Imagine a junior analyst on your team hands you a validation writeup with a conclusion you don't trust. How do you handle giving feedback while still supporting their development?

mentorship style and whether they can correct work without discouraging junior staff

Walk me through how you'd design an early warning alert for a model that's starting to underperform in production. What would trigger it and who would you notify?

practical grasp of ongoing model monitoring and escalation paths, not just one-time validation

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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: As a Lead Data Scientist, you will be one of Enova’s most valuable team members. You will develop, enhance and test the company’s models for use in determining the appropriate lending criteria and verification procedures. You will lead and perform independent model validations to evaluate model fit for purpose, conceptual soundness, mathematical theory and assumptions, data/assumptions, and output reasonableness. At times, you may be asked to conduct ad hoc analysis using statistical and financial tools to recommend for risk management, marketing and operational strategies. You will demonstrate the ability to interpret and organize data, and communicate it effectively to cross functional teams, and provide technical guidance and mentorship to other team members to solve business problems, define requirements and support implementation. Responsibilities: <ul> <li>Lead and perform model validation of credit risk models</li> <li>Prepare model validation reports and write technical documentation</li> <li>Provide technical guidance and mentorship to team members in validating and preparing models for production</li> <li>Interact with model stakeholders and business partners to gather information about credit models and risk acceptances and provide effective challenge when appropriate</li> <li>Support model releases to ensure they are quickly and accurately put into production</li> <li>Monitor model performance dashboards, build alerts, and escalate concerns as appropriate</li> <li>Drive improvements to model validation methodologies, processes, and governance practices by leveraging data manipulation, machine learning, and programming skills to enhance automation and develop custom tools</li> </ul> Requirements: <ul> <li>Experience in developing and/or independently validating advanced credit risk models</li> <li>5+ years of experience in quantitative analysis in the financial services industry and experience leading complex analytical or model validation projects is preferred</li> <li>Strong knowledge of statistical models and methodologies used in risk management</li> <li>Advanced programming skills in Python and the ability to write customized programs for meaningful data analysis</li> <li>Experience working with relational databases, such as SQL</li> <li>Demonstrated ability to provide technical guidance and communicate complex quantitative concepts to technical and non-technical stakeholders</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 (Hybrid)

Likely interview questions for this role

Practice this interview out loud.

The full job description

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