Staff Data Scientist

In this role, you will lead the design, development, and optimization of advanced machine learning models that detect fraud and abuse across massive, high-velocity datasets. You will operate at the intersection of statistics, security, and production engineering, ensuring models remain resilient against evolving adversarial behavior.

• Architect and own advanced machine learning strategies for fraud detection, including payment fraud, identity abuse, account takeover, and network manipulation
• Translate complex fraud and security signals into scalable modeling approaches that balance accuracy, robustness, and business impact
• Design and maintain production-grade feature engineering pipelines informed by deep understanding of attacker behavior and system vulnerabilities
• Establish model evaluation, monitoring, and diagnostic frameworks to detect performance degradation, data drift, and adversarial adaptation
• Lead experimentation and statistical research to uncover new fraud patterns and validate signal effectiveness in production environments
• Partner with ML engineers and security teams to build adversarially robust systems and ensure seamless model deployment and performance
• Leverage AI tools to accelerate experimentation, automate analysis workflows, and improve modeling efficiency while maintaining statistical rigor

Requirements

This role requires deep expertise in statistical modeling, machine learning, and fraud/security domains, combined with strong production experience and the ability to operate in adversarial environments.

• 5+ years of hands-on data science or machine learning experience with ownership of production models at scale
• Strong domain expertise in fraud, cybersecurity, or adversarial systems (e.g., payment fraud, identity abuse, account takeover, network attacks)
• Advanced understanding of statistical modeling, including bias-variance tradeoffs, hypothesis testing, and model diagnostics
• Experience with multiple ML paradigms including tree-based models (XGBoost, LightGBM), deep learning (CNNs, RNNs, transformers), and graph-based methods (GNNs)
• Proven ability to diagnose production model failures caused by drift, adversarial adaptation, or feature leakage
• Strong programming skills in Python and experience working with large-scale data environments
• Ability to translate ambiguous fraud problems into structured modeling and experimentation frameworks
• Experience using AI tools (LLMs, AutoML, or similar) to accelerate feature engineering and analysis while maintaining validation rigor
• Advanced degree in a quantitative field (or equivalent industry experience with deep statistical modeling exposure) preferred

Benefits

• Competitive compensation with performance-based incentives
• Equity opportunities in a high-growth AI company
• Comprehensive health, dental, and vision insurance
• Flexible remote work environment across the United States
• Opportunities to work on large-scale, real-world fraud and security problems
• Strong learning culture with exposure to advanced ML, AI, and security domains
• Collaborative environment with ML experts, engineers, and fraud specialists
• Career growth in a high-impact, research-driven data science organization.

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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