Analyze fraud patterns and trends in electronic payments to identify emerging attack vectors and actionable insights.
Design, test, and implement new fraud models and strategies, including rules and machine learning approaches, to improve detection and minimize false positives.
Develop and optimize fraud and credit risk models for additional products and channels, ensuring solutions are scalable and maintainable.
Execute the full modeling lifecycle, including data collection, data quality checks, feature engineering, model development, validation, performance monitoring, and production deployment.
Present analytical findings, model performance, and recommendations to internal stakeholders and clients, translating complex statistical concepts into clear, business-focused messages.
Investigate and evaluate new data sources to determine their predictive value and integrate them into existing and new fraud models where appropriate.
Collaborate in the design and development of new products and services related to fraud detection and prevention, advising on data and modeling requirements.

2+ years of analytics experience in risk, fraud, or credit domains using tools such as Python, SQL, or similar, including end-to-end work with large datasets.
2+ years of experience in developing and deploying predictive models, including classification models, within financial services, payments, fintech, or a related industry.
Experience designing and executing the full modeling cycle, including data collection, exploratory data analysis, feature engineering, model estimation, performance evaluation, and production implementation using tools such as Python (for example, Pandas, scikit-learn, TensorFlow).
Experience explaining statistical and machine learning concepts to non-technical stakeholders in client-facing or cross-functional settings, including the ability to create clear visualizations and presentations.
Experience writing production-quality Python code to collect and prepare data, estimate models, summarize results, and deliver code for model deployment in collaboration with engineering or operations teams.
Experience using SQL (for example, PL/SQL or similar) to develop queries, scripts, or stored procedures to support automated data collection and model input pipelines.
Bachelor’s degree or higher in Statistics, Mathematics, Engineering, Finance, Data Science, Computer Science, or related field or equivalent combination of education, related experience and/or military experience.

Fuel Your Life program to support your physical, financial, social, and emotional well-being.
Paid holidays and generous time away policies.
No-cost mental health support through Employee Assistance Programs.
Living Proof program to recognize your peers’ extra effort with points redeemable for rewards.
Eight Employee Resource Groups to foster a collaborative culture and expand your network.
Unparalleled professional growth with training, development, and internal mobility opportunities.
Medical, dental, vision, life, and disability insurance options available from day one.
Retirement planning and discounted shares with the Employee Stock Purchase Plan.
Tuition assistance and reimbursement program.
Paid parental, caregiver, and military leave.

E-Payments Risk Data Scientist, Machine Learning

Key skills