Data Analyst, Trust & Safety

United StatesRemote

sql$105,000 - $145,000

Requirements

Resend is building the most accessible email platform for developers.
Looker, Mode, Metabase, BI) - Comfortable working with large event/data sets - Strong sense of measurement: you build metrics that drive decisions - Good communicator—can explain nuanced analytics to non-data folks - You thrive in ambiguity, can build frameworks out of thin air -
Experience with estimation, sampling, statistical validation - Familiarity with prediction and classification models - Comp: $105,000 - $145,000 USD, commensurate with

- 3+ years in data or analytics roles (ideally in security, fraud, or trust and safety) - Fluent in SQL + data tooling (e.g.

experience LEARN MORE ABOUT HOW WE WORK IN TRUST AND SAFETY: - https://resend.com/philosophy - Trust & Safety investment blog post - https://resend.com/handbook/success/what-is-our-philosophy-for-anti-abuse - https://resend.com/handbook/engineering/what-is-our-tech-stack ABOUT RESEND We are building the modern email sending platform for developers.
Meet the humans → https://resend.com/humans

With just an address, anyone can talk to anyone, no matter their location, status, or class.
It’s democratized access, it’s a sacred privilege, and it’s worth protecting at all costs.
As we’ve grown to 2M+ users and continue to onboard thousands of new users every day, the pressure of maintaining a safe place for good senders continues to build up.
As the first Data Analyst on our Trust & Safety squad, you will wear many investigative hats— identifying abuse patterns, analyzing behavioral signals, and developing scalable detection systems that prevent harmful users and enable good senders and scale.
IN THIS ROLE, YOU WILL… - Root-cause investigations of unexpected events or spikes - Deeply understand and stay up to date on email abuse vectors and trends - Define, instrument, and monitor key metrics: time to detect, false-positive rate, coverage, enforcement latency - Build dashboards and reports for real-time and historical visibility into behavior and policy violations - Analyze signal effectiveness: precision, recall, drift, overlap, etc - Work with data engineering to shape data pipelines (ensuring