📍
San Francisco, CA

Data Analyst, GenAI Marketplace

3 years experience
Technology & Digital
Data / Analytics
Posted:
December 28, 2025

Scale

Data labelling and model evaluation platform
72.7
Palpable Score
Apply >view company >

Scale is looking for a Senior Data Analyst to help advance AI development in the Generative AI sector. This role will partner with leadership, operators, and engineers in our Growth Operations team to help unpack ambiguous problems and build lasting, scalable analytics solutions. Your work will directly evolve how Scale operates and measures its growth strategies, funnels, and pipelines. The ideal candidate is detail oriented, rigorous about validating results, talented at distilling down complexity, and loves tackling and solving hard problems.

You will:

Ideally You'd Have:

About the company

Scale

Company overview
Scale builds data infrastructure and tooling used to train, evaluate, and deploy AI systems, including work tied to RLHF, model evaluation, and enterprise AI workflows. Scale sells products like the Scale Data Engine and supports both private-sector and government customers building AI applications. Scale positions the company around “reliable AI systems” and operational excellence alongside software. Scale also runs a large set of roles across engineering, applied AI, operations, and go-to-market teams tied to AI delivery.

Locations and presence

Scale lists San Francisco as the headquarters and commonly hires into hubs like San Francisco and New York, with some roles also listing Seattle. Scale’s careers pages tend to specify location on each role rather than publishing a single, company-wide remote or hybrid policy in one place.

Palpable Score

72.7
/ 100
Scale has real early-career entry points through a dedicated university hub, recurring intern and new grad roles, and a named new grad program for Strategic Projects. Scale is better than many AI startups on transparency, with a published SWE hiring flow and a salary band on at least one flagship new grad posting. Early-career outcomes and stability are the main limiters because public signals point to high intensity and the company has had recent layoffs, while early-career conversion and promotion metrics are not published.
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