Senior Data Scientist, ML— Fraud Detection & Effectiveness

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About the role

Senior Data Scientist, ML— Fraud Detection & Effectiveness The Opportunity We are seeking an experienced Senior Machine Learning Data Scientist to build fraud and abuse detection models and measure how effectively they work. This role combines hands-on modeling with deep experimentation, evaluation, and analytics to improve detection and quantify business impact. You will work across the fraud lifecycle — from modeling and ground-truth definition to performance measurement, monitoring, and executive-ready insights! What you'll Do • Build and tune ML models for fraud and abuse detection using statistical and classical ML techniques. • Develop robust evaluation frameworks, datasets, and metrics to measure model and mitigation effectiveness. • Analyze false positives/negatives, model drift, and emerging fraud patterns to continuously improve detection. • Define ground truth, labeling approaches, and fraud taxonomies that support reliable model development and evaluation. • Design experiments and evaluate tradeoffs across precision, recall, customer impact, and fraud loss. • Build dashboards and metrics that translate detection performance into measurable business impact. • Pressure-test models and data for leakage, bias, data-quality issues, and other sources of misleading results. • Partner across engineering, product, policy, and risk teams to turn insights into detection improvements and business decisions. What you'll need to succeed • 8+ years in applied Data Science / ML, with experience building and evaluating production ML models. • Strong foundation in statistical and classical ML, experimentation, model evaluation, and performance measurement. • Strong hands-on Python and SQL skills working with large, complex datasets. • Experience with model monitoring, drift, false-positive/false-negative analysis, and imperfect or delayed labels. • Strong data visualization and storytelling skills — able to translate complex analysis into clear insights and recommendations. • Strong analytical judgment, ownership, and ability to operate independently through ambiguity. • Bachelor's or equivalent experience in Statistics, Mathematics, Computer Science, or related field; advanced degree a plus. Preferred Attributes • Experience in fraud, abuse, risk, identity, trust & safety, or other adversarial domains. • Experience with anomaly detection, clustering, behavioral modeling, or prevalence estimation. • Experience with labeling frameworks, weak supervision, active learning, or human-review systems. • Familiarity with LLMs and AI-assisted evaluation/analysis. • Experience evaluating multi-layered risk controls and automated decisioning systems. Hybrid Work Model: This role follows a hybrid schedule, with a minimum of 3 days per week in the office. About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity. Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.  Let’s Adobe together At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values a

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