Data Scientist, Trust & Safety

Responsibilities

• Own the analytical foundation for Trust & Safety, including abuse prevalence, fraud loss, false-positive and false-negative rates, time to detect, time to mitigate, appeal and reversal rates, and verification step-up conversion.
• Build reliable datasets and dbt models that connect product events, account and identity signals, payment activity, infrastructure usage, content classifications, enforcement actions, appeals, and support outcomes.
• Develop and evaluate risk models, rules, and anomaly-detection systems for threats such as phishing, scam hosting, cryptomining, token farming, payment fraud, promotional abuse, and AI-agent exploitation.
• Design rigorous offline evaluations, shadow-mode tests, holdouts, and controlled experiments to measure detection quality and the user impact of new policies, enforcement actions, and progressive verification.
• Define thresholds and decision frameworks that balance abuse reduction, economic loss, customer friction, and false positives across free, paid, and enterprise users.
• Investigate emerging abuse patterns, quantify their impact, identify coordinated behavior, and turn ambiguous signals into clear recommendations for product and engineering teams.
• Develop predictive models that estimate account, device, transaction, workspace, or deployment risk and embed those signals into detection, review, and escalation workflows.
• Partner with Support and Legal to improve case review, appeals, reason-code quality, and feedback loops so human decisions become useful model and policy signals.
• Build monitoring that detects model drift, attacker adaptation, data-quality failures, and unexpected harm to legitimate users.
• Communicate findings clearly to technical and non-technical partners, including the tradeoffs, uncertainty, and evidence behind high-impact decisions.

Required Skills and Experience

• 5+ years of experience in data science, product analytics, fraud, risk, trust and safety, or a related field.
• Strong SQL and Python skills, with experience working with large behavioral datasets and building reliable data models or pipelines.
• Experience developing and evaluating predictive models, experiments, or decision systems, with sound judgment around uncertainty and tradeoffs.
• Ability to turn ambiguous data into clear recommendations and communicate them effectively across technical and non-technical teams.
• Comfort working with imperfect labels, biased samples, and high-impact decisions where false positives matter.
• Use AI tools extensively to increase effectiveness while maintaining a high bar for analytical quality.

Preferred Qualifications

• Experience building or evaluating anti-abuse, fraud, identity, security, spam, integrity, or content-safety systems at scale.
• Built, shipped, and maintained ML models in production (classification, anomaly detection, or risk scoring), including feature engineering on behavioral and transaction data, threshold selection against precision/recall economics, and post-launch monitoring.
• Experience with graph analysis, entity resolution, coordinated-behavior detection, reputation systems, anomaly detection, or risk scoring.
• Experience measuring false positives and enforcement harm, designing human-review workflows, or using appeals and case outcomes as model feedback.
• Familiarity with progressive verification, KYC, account trust, or identity providers such as Prove, Persona, Socure, or Stripe Identity.
• Experience with causal inference methods such as difference-in-differences, propensity score methods, synthetic control, or uplift modeling.
• Experience with a modern data stack such as dbt, BigQuery, Snowflake, Fivetran, Amplitude, Mixpanel, or Segment.
• Experience at a consumer platform, developer tool, cloud provider, marketplace, fintech company, or other product with a meaningful adversarial surface.

Bonus Points

• Built AI-powered analytical tools, investigation systems, automated detections, or novel measurement approaches.
• Experience with AI-native abuse such as prompt injection, LLM token farming, model extraction, or agent-driven abuse.
• Understand freemium, usage-based, or promotional pricing models and the abuse vectors they create.