Data Scientist, Trust & Safety

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation.

About the Role

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. Realizing that vision requires a platform that legitimate users can trust and adversarial actors cannot exploit. We're hiring a Data Scientist to help build Replit's Trust & Safety and Anti-Abuse program from the ground up. 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. 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.

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.

Who You Are

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 pressure-test your own work, quantify uncertainty, and distinguish correlation from evidence strong enough to justify enforcement.

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 Will

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.

Examples of What You Could Do

Build a measurement framework for Replit's abuse surface, reconcile incomplete labels across automated detections, human review, appeals, chargebacks, and support cases, and establish a trustworthy baseline for the first time.

Design and evaluate a risk-scoring model for suspicious account clusters using identity, device, payment, graph, and product-behavior signals, then define thresholds that materially reduce fraud while protecting legitimate users.

Analyze a phishing detection rule that appears highly precise, uncover that it disproportionately bans paying users with legitimate brand references, and redesign its evaluation and review path to reduce false positives.

Measure a progressive verification "ladder of trust," determining when to step users up to additional verification and quantifying the tradeoff between abuse prevented and legitimate-user conversion lost.