Data Operations Lead

Sieve•San Francisco, CA

11d•Onsite

About The Position

As Data Operations Lead, you'll own the day-to-day execution and scaling of Sieve's data operations platform. This is a deeply operational and semi-technical role. You'll manage our human workforce, build and improve QA processes, handle people sourcing and onboarding, and drive product ops initiatives that make our platform more efficient. A major part of this role is growth: you'll run campaigns and experiments to expand the platform's user base, find new channels for sourcing, and drive adoption. This role is ideal for someone who is both a builder and an optimizer, someone who can get their hands dirty with tooling while also thinking strategically about how to scale a complex operational machine.

Requirements

Mixed technical and non-technical skillset, comfortable with data tooling, light scripting, and spreadsheet-level analysis
Strong organizational skills and attention to detail; able to manage multiple concurrent work streams
Growth mindset: experience running or contributing to user acquisition, sourcing campaigns, or platform growth efforts
Bachelor's degree in CS, STEM, or equivalent practical experience
In-person at our SF HQ

Nice To Haves

Experience managing human-in-the-loop data operations or annotation pipelines
At least 1 year of engineering experience or strong technical fluency
Experience as an early hire at a startup or spearheading ops at an AI lab
Familiarity with data quality frameworks or ML data pipelines

Responsibilities

Operate and scale Sieve's internal data ops platform, including workforce management, task assignment, and QA workflows
Drive platform growth: run acquisition campaigns, test new sourcing channels, and grow the user base through creative and scalable strategies
Source, onboard, and manage a distributed human workforce for data annotation, curation, and quality review
Build and improve QA processes to ensure data output meets the standards required by frontier AI labs
Own product ops for the data platform. Work with engineering to ship tooling improvements, track operational metrics, and identify gaps
Create documentation, SOPs, and training materials for operational workflows