Data Science Engineer, Payment Risk

The Opportunity Adobe is building a global payments function and recruiting a seasoned payment risk specialist to join our team dedicated to managing payment fraud and related risks. You will bring both risk strategy experience from an online merchant and deep analytical capabilities to this role. Your ability to pull and analyze data will help you find ways to improve Adobe.com risk decisioning at checkout. This will allow you to block BOT attacks and prevent fraudulent transactions. You’ll balance the sensitive ratio of false positives to false negatives, and evolve prevention strategies to drive Adobe’s business objectives. You will help define payment risk policies and build programs that prevent, detect, identify and resolve fraudulent activity by customers, potential customers, and outside agents. You will support outreach programs to the large issuing banks around the world to influence how they decision Adobe transactions. You will define A/B tests and monitor performance with a control group. This improves how you identify false positives globally using statistical methods to enable better decision-making. What You'll Do Mitigate fraudulent payment activity involving credit/debit cards, SEPA, PayPal, and other payment methods worldwide. Collaborate with Adobe’s third-party risk assessment partner and internal ML team. Partner closely with internal collaborator organizations across Adobe including Risk/Abuse team, Chargeback team, ML Decisioning Engineers, Product Management, Payments Partnerships and Finance. Establish Fraud Prevention capabilities based on predictive decisioning models that you will build and maintain for Adobe to align with industry best-practices. Implement improvements that raise the barriers against new threats by influencing the revision of business processes and decision-making capabilities, and through detailed auditing and validation of these processes following implementation of these changes. Bring a “disciplined, analytical and methodical” approach to a space that can be highly reactionary and chaotic from the ever evolving fraudulent attacks in the online space. Define and manage risk control measurements, implement quantitative monitoring metrics, and align internal risk teams and external risk decisioning providers on risk control numeric goals, promote results-focused, data-driven data science practices. Experiment experimental build & hypothesis testing control groups What you need to succeed Hands-on experience (risk industry) with deep technical fluency and practical experience building and operating analytic systems: Strong hands-on SQL and Python skills — You’ll be writing queries, building data pipelines, debugging code, and driving insights from large, complex datasets. Proficiency in both SQL and Python is essential to operate effectively in this role (SQL/Python is a MUST). You should have practical experience in overseeing payment risk, fraud, or abuse operations. Direct knowledge of developing, implementing, and revising risk strategies in these areas is essential. Background in Payment Risk, Fraud, or Abuse is a plus. This includes driving fraud detection logic, tuning decision engines, and managing false positive/negative trade-offs. Practical machine learning experience — A solid track record applying machine learning in production, including model development, validation, deployment, or serving infrastructure, is a strong plus. You should be comfortable operationalizing models, interpreting results, and partnering with ML/engineering teams to integrate models into risk decisioning workflows. In addition to these core requirements, successful candidates will demonstrate: A data-driven approach with the ability to translate analytical outcomes into business decisions and operational controls. The ability to influence teams across multiple functions and partner with product, engineering, and external risk providers. Comfort with defining performance metrics, A/B tests, and ... (truncated, view full listing at source)