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# Data Scientist - Fraud Prevention & Risk Analytics

- **Company:** Acima Leasing
- **Location:** Draper, UT, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Standard Sql, Feature Engineering, Pandas, Scikit Learn, Information Technology, Xgboost
- **Published:** June 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=6cdb4546218733af

## About the Role

Do you have experience in XGBoost?, Do you have a Master's degree?, * 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 )

PREFERRED QUALIFICATIONS

 * 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

## Description

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., * 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.


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