Data Scientist
koahlabs · San Francisco ·
- Work mode
- Hybrid
- Category
- Data science
koahlabs · San Francisco ·
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