Lead Data Scientist - Fraud Analytics (Hybrid)

Enova; Chicago (IL); Hybrid; USD 140,000 - 170,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

PTO & paid holidays off

Sabbatical program (eligible roles)

Summer hours (eligible roles)

Paid parental leave

DEI groups (B.L.A.C.K. @ Enova, HOLA @

Enova

Job summary

Enova is seeking a Lead Data Scientist for its Fraud Analytics team in Chicago. You will develop, deploy, and monitor models and pattern-recognition pipelines to surface emerging fraud trends across lending products, and you’ll collaborate with Fraud Operations to refine features and reduce false positives.

You’ll own models end-to-end—from design through deployment and monitoring—work with Python and SQL on large-scale data, mentor teammates, and translate analysis into strategic business

Qualifications

• Hands-on fraud experience — fraud analytics, fraud strategy, or risk modeling.
• 5+ years of experience in analytics, applied machine learning, or quantitative modeling.
• 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.

Responsibilities

• Develop, deploy, and monitor models and pattern-recognition algorithms to detect emerging and shifting fraud trends across one or more lending products.
• Write Python programs for data analysis and predictive modeling, and query large, complex datasets in SQL.
• Partner with Fraud Operations through the full detection loop, surfacing patterns and refining features to reduce false positives/negatives.
• Communicate findings clearly to cross-functional partners and support implementation.

Skills

Fraud analytics

Leadership

Python

SQL

Machine learning

Data analysis

Stakeholder communication

Job description

Enova is seeking a Lead Data Scientist for its Fraud Analytics team in Chicago. You will develop, deploy, and monitor models and pattern-recognition pipelines to surface emerging fraud trends across lending products, and you’ll collaborate with Fraud Operations to refine features and reduce false positives.

You’ll own models end-to-end—from design through deployment and monitoring—work with Python and SQL on large-scale data, mentor teammates, and translate analysis into strategic business