data
Posted 1 weeks agoStaff Data Scientist
at Sift
United StatesRemote
Responsibilities
- Your infosec depth means you're fluent in threat modeling conversations with security teams, not learning it on the job. - Build automated workflows that scale human expertise while respecting fraud complexity.
Requirements
- 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.
- 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 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 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 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 leverage AI-assisted tools (LLMs, AutoML frameworks) to accelerate experimentation while maintaining verification checkpoints informed by your domain knowledge.
- You'll become the SME on where humans and AI each belong in fraud modeling pipelines.
- WHAT WILL MAKE YOU A STRONG FIT: - Deep, hands-on knowledge of fraud and information security patterns.
- experience with production accountability.
- experience comes from adversarial or security-adjacent domains. - Deep expertise in multiple modeling paradigms: Tree-based methods (XGBoost, LightGBM with parameter mastery), deep learning architectures (CNNs, RNNs, transformers for sequential/graph data), and graph-based methods (GNNs, message passing, network propagation).
- You've chosen frameworks based on problem structure, not trend. - Advanced degree in Statistics, Data Science, Machine Learning, or equivalent (MS or PhD in quantitative field, or 8+ years of demonstrable statistical modeling depth in production fraud/security contexts).
- You know the difference between a model that's broken and one that's working correctly but facing a new fraud strategy. - Proven ability to partner with AI-assisted automation tools.
- You use LLMs, AutoML, and other AI systems to move faster—especially for feature engineering exploration and pattern discovery—but you verify their outputs and know where they hallucinate or oversimplify.
Experience
- Success looks like: Models that outperform baseline by measurable margins because you engineered features informed by years of fraud pattern understanding.
- You're not learning fraud from blog posts; you're bringing operational context from having debugged production systems under attack. - 5+ years of hands-on modeling
Benefits
- You've shipped models to millions of users, owned their performance in production, and made decisions based on what's broken and why—not just benchmark scores. Bonus: some of that
Additional details
- 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.
- 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).
- 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.