Hybrid Fraud Analytics Engineer

Enova; Chicago (IL); Hybrid; USD 63,000 - 95,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

401(k) matching

PTO & paid holidays

Sabbatical program

Summer hours

Paid parental leave

DEI groups

Employee recognition

Charitable matching & volunteer day

Job summary

Enova International is seeking an Analytics Analyst to join the Fraud Analytics team. You will develop, deploy, and monitor models to detect fraud across lending products and work with Fraud Operations to refine features based on investigations.

You will write Python programs, query large datasets with SQL, and communicate findings to cross-functional partners while improving underwriting from a fraud-risk perspective.

Qualifications

• Bachelor's degree in a quantitative field or equivalent experience.
• Solid programming skills for data analysis demonstrated through work or projects.
• Working knowledge of relational databases (SQL).
• Foundational knowledge of statistical methods and machine learning.
• Interest in fraud, fintech, or lending is a plus.

Responsibilities

• Develop, deploy, and monitor models and pattern-recognition algorithms to detect fraud trends.
• Write Python programs for data analysis and predictive modeling; query large datasets with SQL.
• Collaborate with Fraud Operations to refine features and reduce false positives/negatives.
• Conduct ad hoc analyses on large datasets to scope new fraud trends and risks.
• Clearly communicate findings to cross-functional partners and support implementation.
• Assist in improving underwriting from a fraud-risk perspective.

Skills

Programming for data analysis

SQL

Statistics knowledge

Machine learning basics

Analytical mindset

Education

Bachelor's degree in a quantitative field

Tools

Python

SQL

Job description

Enova International is seeking an Analytics Analyst to join the Fraud Analytics team. You will develop, deploy, and monitor models to detect fraud across lending products and work with Fraud Operations to refine features based on investigations.

You will write Python programs, query large datasets with SQL, and communicate findings to cross-functional partners while improving underwriting from a fraud-risk perspective.