The opportunity
We're redefining how software is built and who gets to build it. Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI.
What you'll do
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.
What they're looking for
- 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.
