Lead Data Scientist, Fraud Analytics

Enova; United States; Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Sabbatical program

PTO & paid holidays off

Paid parental leave

401(k) matching

Job summary

Enova is seeking a Lead Data Scientist for its Fraud Analytics team to drive the quantitative engine behind fraud prevention across lending products. You will develop, deploy, and monitor models and pattern-recognition algorithms, writing Python programs and querying large datasets with SQL, while partnering with Fraud Operations to refine features and reduce false positives.

You will mentor teammates, translate analyses into business strategy, and communicate findings to senior stakeholders in

Qualifications

• 5+ years of experience in analytics, applied machine learning, or quantitative modeling
• Leadership experience mentoring teammates and driving initiatives
• Advanced Python and SQL; end-to-end modeling on large-scale data
• Hands-on fraud experience in fintech or lending
• Track record translating analysis into business strategy and communicating with senior stakeholders

Responsibilities

• Develop, deploy, and monitor models and pattern-recognition algorithms to detect emerging fraud trends across lending products
• Write Python programs for data analysis and predictive modeling and query large datasets in SQL
• Partner with Fraud Operations through the full detection loop to refine features and reduce false positives/negatives
• Conduct ad hoc analysis on large, complex datasets to scope new fraud trends and recommend risk strategies
• Communicate findings clearly to cross-functional partners and support implementation
• Help improve underwriting and verification processes from a fraud-risk perspective and apply AI in production

Skills

Python

SQL

Fraud analytics

Machine learning

Leadership

Stakeholder communication

Tools

Python

SQL

Job description

Enova is seeking a Lead Data Scientist for its Fraud Analytics team to drive the quantitative engine behind fraud prevention across lending products. You will develop, deploy, and monitor models and pattern-recognition algorithms, writing Python programs and querying large datasets with SQL, while partnering with Fraud Operations to refine features and reduce false positives.

You will mentor teammates, translate analyses into business strategy, and communicate findings to senior stakeholders in