Marketplaces · Experimentation, Forecasting & Applied ML, Data Platforms & Reliability
Fraud detection uplift
Dating Marketplace
Rebuilt trust & safety analytics and launched fraud models that cut abusive users from 10% to 0.6%.
Problem
Large volumes of short lived accounts made manual review ineffective. Losses, user trust, and compliance targets were at risk.
Solution
- Data pipeline overhaul (dbt + Airflow + ClickHouse) to unify trust & safety signals
- Python/Flask microservices with retraining workflow, champion/challenger evaluation, and governance
- Shadow evaluation, playbooks, and enablement to launch safely across regions
- Established daily standups with product, security, and leadership to keep scope tight and surface regulatory feedback.
Outcome
- Significant uplift in recall at the target precision alongside clear business guardrails
- ~40 days earlier average detection of bad actors with automated actioning
- Weekly exec reporting and clear SLOs for pipeline health and model drift