Principal Applied Scientist

Independently lead and execute multiple high-impact fraud detection initiatives, turning ambiguous problems into measurable business outcomes. Develop practical, deployable machine learning (ML) solutions that operate reliably at production scale, balancing detection effectiveness, latency, resilience, operational complexity, and cost. Translate scientific insights into production impact through rigorous experimentation, validation, rollout, monitoring, and continuous optimization under real-world operating constraints. Apply advanced ML methods—including anomaly detection, cross-signal analysis, large language models (LLMs), and other modern AI techniques—with clear evaluation frameworks, robust tests, and success metrics to deliver reliable, scalable, production-ready solutions. Drive technical collaboration across science, engineering, ads, security, and privacy teams to operationalize research and deliver end-to-end fraud detection capabilities. Raise the technical bar through mentorship, scientific rigor, responsible AI practices, and high standards for quality, reliability, and governance. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field ] 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers). 2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker. 5+ years experience conducting research as part of a research program (in academic or industry settings). 3+ years experience developing and deploying live production systems, as part of a product team. 3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping. Extensive industry experience delivering machine learning solutions to production, including technical leadership in complex, ambiguous problem spaces. Proven track record architecting scalable ML systems and low-latency decision pipelines that operate reliably and cost-efficiently at web scale. Deep expertise in several of the following areas: statistical machine learning, anomaly detection, fraud and risk modeling, deep learning, large-scale data mining, and causal inference. Demonstrated ability to set technical direction, influence cross-functional partners, and drive multi-year strategy across research and engineering teams. Solid software engineering and system design skills, including model operationalization, experimentation frameworks, and production quality standards. Experience mentoring and developing scientists, with a history of raising scientific rigor and execution quality across teams. Excellent communication and stakeholder management skills, with the ability to translate complex technical concepts into business impact. Research impact through publications, patents, or significant internal innovations in fraud detection, anomaly detection, or related ML domains.