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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 What the job involves • 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 , • 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 , • 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