Staff Data Scientist

Role Description

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:

• Spot feature opportunities others miss.
• Anticipate how adversaries will probe your models.
• Translate customer fraud signals into modeling advantage.

Success looks like:

• Models that outperform baseline by measurable margins.
• Production systems that don't degrade and don't leak money to evolving fraud schemes.
• Teams that trust your framework recommendations.
• A research program that uncovers untapped signal in our customer data.

Qualifications

• Deep, hands-on knowledge of fraud and information security patterns.
• 5+ years of hands-on modeling experience with production accountability.
• Deep expertise in multiple modeling paradigms.
• Advanced degree in Statistics, Data Science, Machine Learning, or equivalent.
• Lean, deep statistical intuition informed by domain reality.
• Proven ability to partner with AI-assisted automation tools.
• Comfort working in ambiguity and adversarial contexts.

Requirements

• Architect and own advanced modeling strategies across fraud and abuse problem domains.
• Establish and defend model quality standards that account for adversarial dynamics.
• Lead statistical innovation on our highest-leverage fraud problems.
• Partner with ML engineering and information security on adversarial robustness.
• Build automated workflows that scale human expertise while respecting fraud complexity.

Benefits

• At Sift, we are intentionally building a diverse, equitable, and inclusive workplace.
• We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need.
• We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences.

Company Description

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.