Data Scientist

Clerk•San Francisco, CA

14d

About The Position

Clerk is hiring a Staff-level Data Scientist or ML Engineer to own machine learning and the product-facing side of the company’s data. The role joins a deliberately small, senior data team and focuses on taking ambiguous, greenfield problems from the initial question through production, working in both data and engineering repositories. The person will build machine-learning infrastructure, including pipelines, training, and model serving that fit Clerk’s existing engineering stack. They will turn internal data signals—such as conversion and expansion scoring, lifetime value, and feature telemetry—into products, including tools that give customers natural-language access to their data. The role also involves shipping models and signals in product and platform code alongside the teams responsible for those systems, and driving cross-team work. This is a high-autonomy individual-contributor role for someone who combines building and analysis, works iteratively, and is willing to take on a broad range of work to help Clerk succeed. The position is for someone in the Americas within ±3 hours of U.S. Mountain Time. Familiarity with Clerk’s stack (GCP/BQ, dbt, Dagster, ClickHouse, PostHog, and Hex) is expected or should be quick to acquire; developer tools or infrastructure experience is a plus.

Requirements

Staff-level Data Scientist or ML Engineer experience.
Has built machine-learning systems from scratch and operated them in production.
Comfortable both building systems and analyzing data.
Experience owning large, ambiguous problems as an individual contributor, with a desire for substantial scope and autonomy.
Comfortable working across data and engineering, including contributing to adjacent engineering repositories and collaborating with their teams to ship work.
Product sense to identify worthwhile problems.
Iterative, ship-and-iterate working style suited to a fast-growing startup.
Technical fluency and high standards for rigorous data, machine-learning, and infrastructure work.
Located in the Americas within ±3 hours of U.S. Mountain Time.
Familiar with the GCP/BQ, dbt, Dagster, ClickHouse, PostHog, and Hex stack, or able to learn it quickly.

Nice To Haves

Experience in developer tools or infrastructure products.

Responsibilities

Build machine-learning infrastructure, including production pipelines, model training, and model serving using Clerk’s existing engineering stack.
Develop products from internal data signals, including conversion and expansion scoring, lifetime value, and feature telemetry.
Ship useful data products to customers, including a semantic layer for natural-language access to their data.
Integrate models and data signals into product and platform code in engineering repositories, working with the teams that own those systems.
Drive cross-team work to deliver machine-learning and data initiatives.
Take on other work that helps Clerk succeed, such as building dashboards, investigating trends, or speaking with customers.