Data Scientist (Trust & Safety)

Join Replit, a platform redefining software development and making programming accessible to all. As a Data Scientist in Trust & Safety, you'll build and implement systems to protect users and the platform from abuse. You'll work closely with various teams to balance abuse reduction and user experience. Your responsibilities will include developing risk models, designing evaluations, and investigating emerging abuse patterns. This is a remote-first position with flexible hours and a range of benefits.

• Transform noisy behavioral, identity, payment, infrastructure, and content signals into measurement systems, detections, and decisions that protect Replit's users.
• 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.
• 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.
• You're a data scientist who moves fast, goes deep, and thinks adversarially. You can spin up an analysis in hours that would take others days, not by cutting corners, but because you've built the intuition and technical toolkit to get to the right answer quickly
• You dig past the top-line abuse rate to understand selection effects, missing labels, policy changes, attacker adaptation, and the false positives hidden inside an aggregate metric
• You understand that Trust & Safety data is imperfect and outcomes are high stakes. Ground truth is delayed, biased, and often incomplete; attackers react to defenses; and an apparently effective rule can quietly harm legitimate users
• You use AI agents and tools aggressively to multiply your output: writing code, exploring data, generating hypotheses, and prototyping investigations. But you treat every AI-assisted output as a draft, not a deliverable. You know what good analysis looks like and won't ship anything that doesn't meet that bar
• You pressure-test your own work, quantify uncertainty, and distinguish correlation from evidence strong enough to justify enforcement
• Strong SQL and Python skills, with experience working with large behavioral datasets and building reliable data models or pipelines
• You use AI tools extensively to increase your effectiveness while maintaining a high bar for analytical quality
• Experience developing and evaluating predictive models, experiments, or decision systems, with sound judgment around uncertainty and tradeoffs
• Comfort working with imperfect labels, biased samples, and high-impact decisions where false positives matter
• 5+ years of experience in data science, product analytics, fraud, risk, trust and safety, or a related field
• Ability to turn ambiguous data into clear recommendations and communicate them effectively across technical and non-technical teams
• 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
• You've built AI-powered analytical tools, investigation systems, automated detections, or novel measurement approaches
• You have experience with AI-native abuse such as prompt injection, LLM token farming, model extraction, or agent-driven abuse
• You've worked directly with operational review teams and can translate analytical signals into practical playbooks, queues, and escalation paths
• You understand freemium, usage-based, or promotional pricing models and the abuse incentives they create