Sift

Staff Data Scientist

Remote - USA · Staff+

Sponsorship not specifiedDetected 43 days ago

Machine LearningDeep LearningData ScienceNLPLLMsStatisticsA/B TestingCybersecurityCustomer SuccessResearchLeadershipProblem SolvingMentoring

About the role

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.
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.

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.
Lead statistical innovation on our highest-leverage fraud problems.
You'll run rigorous experiments to validate whether a suspected fraud pattern is exploitable or a false lead.
Build automated workflows that scale human expertise while respecting fraud complexity.
You'll document which automation patterns you trust (feature engineering exploration) and which require human oversight (fraud strategy pivots that might break assumptions in your features).
Proven ability to partner with AI-assisted automation tools.
You're building intellectual scaffolding, not outsourcing judgment.
Hiring Manager interview (45 minutes) - Deep dive into your approach to model design, production failure diagnosis, and threat model thinking. Discuss a time a production model failed and how you debugged it in the context of evolving attacker tactics.

Deep, hands-on knowledge of fraud and information security patterns.
You can explain the difference between a velocity signal that's correlated with fraud and one that's causal-and why attackers can't simply game it.
5+ years of hands-on modeling experience with production accountability.
We care more about statistical intuition + proven execution than pedigree.
You can explain why a fraud model is failing through first principles (feature leakage from attacker behavior that changed, distribution shift from geography expansion, optimization pathology from class imbalance).
You know the difference between a model that's broken and one that's working correctly but facing a new fraud strategy.
Comfort working in ambiguity and adversarial contexts.

Production systems that don't degrade and don't leak money to evolving fraud schemes.
A research program that uncovers untapped signal in our customer data while staying ahead of attacker sophistication.

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.

We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need.
Your infosec depth means you're fluent in threat modeling conversations with security teams, not learning it on the job.
You're not learning fraud from blog posts
Bonus: some of that experience comes from adversarial or security-adjacent domains.
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).

Sift is the AI-powered fraud platform securing digital trust for leading global businesses.
Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly.
Global brands rely on Sift to unlock growth and deliver seamless consumer experiences.
Visit us at sift.com http://sift.com and follow us on LinkedIn https://www.globenewswire.com/Tracker?data=XHeK0v8NcNrEkwcDe8QxwpZeCkdQqNyKlni83U-CUmrprdKXWpVlYOAbVzwe2OmlwIUN-q4HXk4hf_dazpHx2NMM1CW_SYj740q9mxXNQI4=.
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.
Success looks like: Models that outperform baseline by measurable margins because you engineered features informed by years of fraud pattern understanding.
Teams that trust your framework recommendations because you've debugged production failures in real fraud contexts.

This listing is sourced directly from Sift's careers page and normalized into a canonical job model.