Lead Data Scientist (Hybrid)

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:

• 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
• Manage team members, and help coordinate their work with business priorities. 

Requirements:

• 5+ 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
• Previous leadership experience mentoring teammates, driving team initiatives, and shaping priorities.

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 here.