Replit

Data Scientist, Trust & Safety

Foster City, CA · Full-time

Sponsorship not specifiedDetected 1 day ago

PythonReactSQLBigQuerySnowflakeMachine LearningdbtData ScienceLLMsAgentic AIDetection Engineering

About the role

Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI.
Realizing that vision requires a platform that legitimate users can trust and adversarial actors cannot exploit.
You'll turn noisy behavioral, identity, payment, infrastructure, and content signals into the measurement systems, detections, and decisions that protect Replit's users, platform, and economics.

We're redefining how software is built and who gets to build it.
We're hiring a Data Scientist to help build Replit's Trust & Safety and Anti-Abuse program from the ground up.
You'll work closely with Engineering, Support, Legal, Security, Infrastructure, Money, and Growth to make abuse economically unviable while keeping friction low for legitimate users.

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 know what good analysis looks like and won't ship anything that doesn't meet that bar.
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.
Comfort working with imperfect labels, biased samples, and high-impact decisions where false positives matter.

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.

making programming accessible, collaborative, and powered by AI.
Replit sits at the frontier of AI-native abuse.
Our platform is a target for phishing and scam hosting, cryptomining, LLM token farming, card and coupon fraud, referral abuse, and increasingly, abuse driven by AI agents themselves.
You'll help define how we identify, measure, and respond to these threats without compromising the experience of good users.
You're a data scientist who moves fast, goes deep, and thinks adversarially.
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 pressure-test your own work, quantify uncertainty, and distinguish correlation from evidence strong enough to justify enforcement.

This listing is sourced directly from Replit's careers page and normalized into a canonical job model.