Senior Data Scientist

Alloy is where you belong! Alloy is the AI-powered identity and fraud prevention platform that accelerates onboarding, stops fraud, and scales compliance across the customer lifecycle so financial organizations can grow without limits. More than 900 of the world's leading financial institutions and fintechs trust Alloy for smarter risk management that drives growth. Through our values: Be Bold, Go Fast, Collaborate, and Celebrate Our Differences, we are creating a workplace where you can grow, thrive, and belong. See how we’ve been continuously recognized and named one of Inc. Magazine’s Best Workplaces, Forbes America’s Best Startup Employers, Best Fintech to Work for by American Banker, year after year. Check out our investors and read more about us here. About the team The Signals team builds Alloy’s real-time machine learning systems at scale. Our immediate focus is on fraud detection, where we believe machine learning can simplify and accelerate decision-making in ways traditional rule-based systems can’t. Managing rules and policies to detect fraud is complex and constantly evolving; we use ML to make it smarter, faster, and more adaptive. Our approach is identity-centric, combining signals from a wide range of data sources to build a comprehensive understanding of risk. You will work on advancing our core models while also partnering directly with customers to driv [... source excerpt omitted ...] comes from fraud studies. Alloy operates in a hybrid work environment. We look to foster collaboration and community by having our local employees onsite three days a week. What you'll be doing Contribute to the design, training, and evaluation of machine learning models that power Alloy’s fraud detection capabilities. Develop testing plans, metrics, performance reports, and translate findings into actionable recommendations. Support production ML workflows, including feature generation, model training, and monitoring, to ensure models remain accurate and reliable at scale. Document findings and communicate insights to internal teams, contributing to shared learning and c [... source excerpt omitted ...] n and move on quickly You have: 8+ years as an individual contributor in Applied Fraud Research, Data Science, or Machine Learning with a proven track record in a “Solutions” or client-facing capacity. Expertise in working with highly imbalanced datasets and building production-grade Machine Learning models with specific interest in tree-based models. Advanced proficiency in scripting languages like Python and querying languages like SQL Proven ability to wrangle and think thoughtfully about data at scale (processing billions of records). Experience developing metrics and dashboards. Able to communicate their findings effectively to technical and nontechnical members So