Senior Data Scientist on Enova's Fraud Analytics team, acting as the quantitative engine of our fraud prevention efforts. You will develop, enhance, deploy, and monitor models and pattern-recognition pipelines to surface emerging fraud trends across one or more lending products, then work closely with Fraud Operations to investigate flagged applications and refine features based on their findings. Responsibilities include writing Python for data analysis and predictive modeling, querying large SQL datasets, and conducting ad hoc analyses to scope new fraud trends and recommend risk and operational strategies. You will communicate findings to cross-functional partners, support implementation, and help improve underwriting from a fraud-risk perspective. You will apply AI in production to streamline fraud prevention processes and mentor team members, coordinating work with business priorities. Qualifications: 4+ years of analytics, applied machine learning, or quantitative modeling; hands-on fraud experience (fraud analytics, fraud strategy, or risk modeling, ideally fintech or lending); advanced Python and SQL; experience owning models end-to-end on large-scale transactional data; ability to translate analysis into business strategy and communicate with senior stakeholders; interest in moving into people leadership through mentoring and leading initiatives. Compensation and benefits: The budgeted annual salary range is $96,000 to $125,000, with potential for a bonus; benefits include health, dental, vision, 401(k) matching with Roth option for U.S. employees, PTO and holidays

Senior Data Scientist on Enova's Fraud Analytics team, acting as the quantitative engine of our fraud prevention efforts. You will develop, enhance, deploy, and monitor models and pattern-recognition pipelines to surface emerging fraud trends across one or more lending products, then work closely with Fraud Operations to investigate flagged applications and refine features based on their findings. Responsibilities include writing Python for data analysis and predictive modeling, querying large SQL datasets, and conducting ad hoc analyses to scope new fraud trends and recommend risk and operational strategies. You will communicate findings to cross-functional partners, support implementation, and help improve underwriting from a fraud-risk perspective. You will apply AI in production to streamline fraud prevention processes and mentor team members, coordinating work with business priorities. Qualifications: 4+ years of analytics, applied machine learning, or quantitative modeling; hands-on fraud experience (fraud analytics, fraud strategy, or risk modeling, ideally fintech or lending); advanced Python and SQL; experience owning models end-to-end on large-scale transactional data; ability to translate analysis into business strategy and communicate with senior stakeholders; interest in moving into people leadership through mentoring and leading initiatives. Compensation and benefits: The budgeted annual salary range is $96,000 to $125,000, with potential for a bonus; benefits include health, dental, vision, 401(k) matching with Roth option for U.S. employees, PTO and holidays