Data Strategy Lead: AI Data Ops & Impact

Cartesia; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Compensation

Health Insurance

Parental Leave

401(k)

Commuter Allowance

Flexible PTO

Meals & Snacks

Your own personal Yoshi

Job summary

Cartesia in San Francisco is seeking a Data & Strategy professional to join the founding team. You will partner with research and modeling teams to identify where models fail, through evals and direct customer conversations, and then fix them by sourcing high quality data.

This role emphasizes technical depth, operational rigor, and high agency, with impact on data pipelines, model evaluation, and revenue growth; in-office collaboration across the US and abroad.

Qualifications

• 3–10 years in high-ownership roles (e.g., founder, GM, or strategy/ops at a startup).
• Technical fluency with data and operations; SQL proficiency required.
• Able to operate with ambiguity and rapid pace; strong attention to detail.

Responsibilities

• Partner tightly with research and modeling teams to find where models fail, through evals and direct customer conversations.
• Design, source, and maintain the evals that define quality for our models. Ground them in observed customer failures, benchmark continuously against other systems, and turn the results into concrete data requirements.
• Establish new sources of human data to advance model performance, building collection pipelines from scratch, and own data quality from acquisition through delivery.
• Stay close to the research. Read papers, understand what datasets move which capabilities, and collaborate with the team on what data to collect next.
• Be commercially minded. Engage with customers, enable the GTM team, and drive revenue growth.

Skills

SQL proficiency

Data operations

High ownership

Education

CS background or related

Job description

Cartesia in San Francisco is seeking a Data & Strategy professional to join the founding team. You will partner with research and modeling teams to identify where models fail, through evals and direct customer conversations, and then fix them by sourcing high quality data.

This role emphasizes technical depth, operational rigor, and high agency, with impact on data pipelines, model evaluation, and revenue growth; in-office collaboration across the US and abroad.