Data Scientist - Fraud Prevention & Risk Analytics

Acima•Draper, UT

6d•Onsite

About The Position

We’re expanding our Fraud Prevention team and seeking a Data Scientist who will specialize in fraud detection and prevention. In this role, you’ll apply advanced analytics, machine learning, and statistical modeling to uncover fraud patterns, safeguard our customers, and protect Acima’s business. You’ll collaborate closely with fraud and data science teams to design proactive solutions, monitor portfolio health, and deliver insights that drive strategic decisions. The work you do will have immediate and lasting impact on both security and customer trust.

Requirements

Bachelor’s or advanced degree in Data Science, Mathematics, Computer Science, Statistics, or related field
5+ years’ experience programming in Python (NumPy, Pandas, Scikit-learn, XGBoost)
2+ years’ experience with SQL
Experience with fraud prevention, detection, or risk analytics
Strong ability to balance fraud loss, customer experience, and portfolio performance
Excellent problem-solving and independent work skills
Experience with fraud prevention platforms (e.g., Kount, CyberSource Decision Manager, Signifyd)
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.

Nice To Haves

Understanding of chargeback dispute/management processes
Knowledge of fraud typologies (card fraud, identity theft, synthetic identity, etc.)
Familiarity with advanced feature engineering for fraud detection
Master’s degree or higher in a quantitative discipline

Responsibilities

Develop and deploy fraud detection models/strategies using Python and ML frameworks (Scikit-learn, XGBoost, etc.).
Engineer and optimize fraud-specific features (e.g., velocity checks, behavioral profiles, device/IP analysis).
Monitor and analyze fraud trends to identify vulnerabilities and recommend improvements.
Partner with Fraud Prevention Manager to design hybrid rules + ML fraud detection strategies.
Share data-driven insights with leadership to influence fraud risk strategy.
Support chargeback dispute/management processes through analytical insights.