Staff Data Scientist, Payments and Fraud

About Courtyard Courtyard. io is the fastest-growing collectibles startup, ever. We are revolutionizing the world of collectibles trading by enabling instant liquidity and delivering a high-velocity, immersive experience. From trading cards to comics, we're redefining how people discover, collect, and unlock value. Courtyard is not just another marketplace. All assets are securely vaulted and fully insured, giving collectors peace of mind alongside unmatched speed and simplicity. Whether you're investing, discovering, or curating your dream collection, we've built a platform that's trusted, simple, and built for speed. And we're just getting started.

We're a remote-first company hiring across all functions to push the boundaries of what's possible in collectibles and digital ownership. Job Summary Courtyard is hiring a Staff Data Scientist to own fraud, risk, and payments analytics and models. This is an embedded role to the payments and fraud team, and it comes with real autonomy: you'll decide what we detect, how we measure loss, and where the tradeoff sits between stopping abuse and getting in a good customer's way. You'll work across engineering, operations, finance, and product. What You'll Do - Own detection end to end — from finding the signal, to the model or rule, to something running in production that changes an outcome - Build the resolution and network analysis that surfaces coordinated behavior rather than one bad account at a time - Own how we measure risk - Partner with marketing and product to pressure-test incentive and referral mechanics before launch, not after - Own the analytical case for our identity and payments risk vendors - Analyze disputes, chargebacks, and authorization performance, and manage the approval-versus-loss tradeoff deliberately - Work with operations on review queue design — what escalates, what gets decided automatically, and where reviewer time is worth spending - Make risk exposure legible to leadership What We're Looking For - 6+ years of industry DS/analytics experience, with meaningful time on fraud, risk, trust and safety, or payments - Self-driven — you find the exposure nobody flagged and come back with a recommendation, not an observation - High-velocity execution: Proven experience building high-volume risk evaluation systems and conducting complex vector analysis. You know how to build rules, run experiments, and hit the ground running. - Comfort operating where labels are late, ambiguous, or contested - Strong graph and network thinking, paired with excellent judgment about asymmetric costs (for example, balancing false positives against fraud loss).