About the role
Lead the AI Economy Trust & Safety project end to end, including problem definition, execution planning, technical reviews, production rollout and measurement. Own the identity-resolution and clustering approach used to connect accounts, tenants and payment instruments to common actors. Own applicant-risk and ongoing account-risk models, including the evidence used for initial decisions and subsequent reassessment. Develop detection methods that distinguish legitimate high-volume use from coordinated extraction and other abusive behavior. Select and evaluate machine learning approaches, including supervised learning, anomaly detection, graph methods, sequence models and large language models. Establish the evaluation framework for model quality, calibration, false-positive impact, drift, explainability and adversarial robustness, and maintain the associated threat model. Work with product and engineering counterparts on the AI Economy team to translate detection findings into product controls, and coordinate with privacy, legal and external dependency owners. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 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: Experience taking an ambiguous, adversarial problem from first framing through to a deployed system, including the evaluation that proved it worked. Prior work on know-your-customer, identity proofing, or payment risk in a self-serve or developer-facing product. A record of mentoring scientists and engineers, and of raising the technical bar across a whole team. Depth across several modeling families, including graph and network methods, anomaly detection, sequence models and large language models, with evidence for why a given choice held up on adversarial data. Experience reasoning about attacker economics: what a given control costs the adversary, and where raising that cost changes behavior. Patents, peer-reviewed publications, or open-source contributions in machine learning, security or risk modeling. 7+ years experience building fraud, abuse, security or risk detection systems that were delivered into a production environment.