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Salary analysis
Compared with the selected benchmark ("All roles in Chicago, United States"), this listing's salary midpoint is about 37% lower. The offer sits below the benchmark range (€9,260–€20,140). The listed pay band (€8,000–€10,417) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 3 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| Market Average: Data Scientist | €3,250/per month | €5,775/per month | €8,681/per month |
| All roles in Chicago, United States | €9,260/per month | €13,890/per month | €20,140/per month |
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 Senior 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.
Job Details
Responsibilities
- Develop, deploy, and monitor models and pattern-recognition algorithms to detect emerging and shifting fraud trends across one or more lending products
- Write customized programs in Python for meaningful data analysis and predictive modeling, and query large, complex datasets in SQL
- 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
- Conduct ad hoc analysis on large, complex datasets to scope new or changing fraud trends and recommend risk, verification, and operational strategies
- Communicate findings clearly to cross-functional partners, provide requirements, and support implementation
- Help improve underwriting and verification processes from a fraud-risk perspective
- Apply AI in production applications to streamline fraud prevention processes
- Mentor and develop team members, and help coordinate their work with business priorities.
Requirements
- 4+ years of experience in analytics, applied machine learning, or quantitative modeling
- Hands-on fraud experience required — fraud analytics, fraud strategy, or risk modeling, ideally in fintech or lending
- Advanced Python and SQL; experience owning models end-to-end — design through deployment and monitoring — on large-scale transactional data
- Track record of translating analysis into business strategy and communicating with senior stakeholders
- Aspiration to grow into a people leadership role through mentoring teammates, driving team initiatives, and shaping priorities.
Skills & Technologies
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