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Koah

Data Scientist

Koah

San FranciscoFull TimeJanuary 22, 2026

Job Description

Who We Are

Koah Labs is building the ad network to power the next generation of AI-native products. Our mission is to help publishers monetize and help advertisers reach the right audience — without compromising speed, UX, or privacy.

We’re a small, tight-knit team in San Francisco with backgrounds at X, Apple, Meta, and early-stage startups. We’ve raised from top investors and are growing fast with real traction on both the publisher and advertiser sides.

Working at Koah means joining at the ground floor: you’ll ship code that shapes the company and the ecosystem we’re building. We move quickly, operate with high trust, and care deeply about craft.



Our Stack

- Infra: Terraform, AWS, LGTM (Loki, Grafana, Tempo, Mimir), Tailscale, Cloudflare

- Data: PostgreSQL, ClickHouse, Redis, Kafka, Python

- Core Application: Ruby on Rails, React, TypeScript

- SDKs: iOS, Android, Web, Flutter, React Native



In this role, you will

- Sit within product engineering and help drive product decisions using data and causal reasoning

- Design, implement, execute experiments and analyze results

- Help level-up all of engineering, encouraging data driven decisions and a deep understanding of the important metrics that drive our business forward

- Use rigorous statistical thinking and hands-on modeling to turn our rich marketplace data into tools that directly shape product decisions and key insights



You might be a fit if

- You have an advanced degree in Physics, Computer Science, Mathematics, Statistics, Engineering, or a related field

- You enjoy identifying and owning challenging problems, forming testable hypotheses, and conducting impactful research to drive significant business impact

- You have a relentless focus on continuous learning and making an impact with an ability to question the status quo

- You have strong mathematical and statistical modeling skills

- You enjoy communicating conclusions to both technical and non-technical audiences alike