Staff Data Scientist

ABOUT THE TEAM: Our Data Science team owns the machine learning backbone of Sift's fraud platform—a system that learns from 1T+ events annually across our network of 700+ global customers. You'll work alongside ML engineers, platform teams, and customer success leads who obsess over reducing false positives while catching sophisticated fraud patterns at scale. We're looking for a specialist who combines exceptional statistical rigor with deep fraud and information security domain expertise. You understand account takeover tactics, payment fraud vectors, identity manipulation, and network abuse patterns—not from reading threat reports, but from having modeled them in production. You'll be the go-to expert for diagnosing why models fail, architecting solutions across multiple modeling paradigms, and building processes that prevent data science from becoming a bottleneck. Your domain knowledge becomes a force multiplier: you'll spot feature opportunities others miss, anticipate how adversaries will probe your models, and translate customer fraud signals into modeling advantage. Success looks like: Models that outperform baseline by measurable margins because you engineered features informed by years of fraud pattern understanding. Production systems that don't degrade and don't leak money to evolving fraud schemes. Teams that trust your framework recommendations because you've debugged production failures in real fraud contexts. A research program that uncovers untapped signal in our customer data while staying ahead of attacker sophistication. WHAT YOU'LL DO: - Architect and own advanced modeling strategies across fraud and abuse problem domains (payment fraud, account takeover, identity spoofing, account abuse, content manipulation, credential stuffing). Your deep understanding of attacker tactics, exploit chains, and evasion strategies informs which signals matter and which are noise. You'll drive framework selection—deciding when gradient boosting on velocity features suffices, when graph neural networks unlock network effects competitors miss, when deep learning on sequence data catches adaptive fraud patterns—and hold yourself accountable for production outcomes. You'll work backward from business metrics (customer adoption, chargeback reduction, operational lift) to model objectives informed by threat models. - Establish and defend model quality standards that account for adversarial dynamics. You'll develop diagnostic frameworks to decompose model performance by fraud type, attacker sophistication level, geography, and temporal patterns. You'll own the post-launch monitoring process, identify when degradation signals retrain vs. architecture change vs. active evasion by fraud rings. You'll design sampling strategies that catch emerging fraud patterns before they scale. Your infosec intuition becomes your quality moat: you'll spot when performance drops aren't random—they're a signal that attackers have found a new exploit path. - Lead statistical innovation on our highest-leverage fraud problems. You'll explore novel feature representations drawn from your understanding of fraud mechanics (network propagation of compromised accounts, timing signatures of automated attacks, behavioral deviation from account history). You'll run rigorous experiments to validate whether a suspected fraud pattern is exploitable or a false lead. You'll publish findings internally (and externally where disclosable), and mentor junior data scientists on the difference between statistical significance and security-relevant signal magnitude. - Partner with ML engineering and information security on adversarial robustness. You'll co-design models that don't just maximize accuracy—they resist manipulation. You'll pressure-test feature importance against known evasion tactics. You'll own the handoff from research to serving, ensuring what ships hasn't leaked assumptions about attacker behavior. Your infosec depth means you're fluent in threat modelin