Data Strategy Lead

Cartesia•San Francisco, CA

1d•$150,000 - $250,000•Onsite

About The Position

Cartesia is seeking a Data Strategy Lead to join their Data Strategy Team, which reports directly to the founders and shapes the company's model capabilities. This role involves working alongside researchers to identify model weaknesses through evaluations and customer feedback, and then rectifying these issues by sourcing high-quality data. The ideal candidate will possess technical depth, operational rigor, and high agency, with a desire to make a significant impact. Cartesia is pioneering AI that learns and interacts with the world like humans, developing novel model architectures like State Space Models (SSMs). The company is well-funded by prominent investors and supported by a network of AI experts.

Requirements

3–10 years of experience in high-ownership roles (e.g., founder, GM, or strategy/ops at a startup).
Technical fluency with data and operations.
SQL proficiency required.
High standards and accountability, with a bias for action and strong attention to detail.
Comfortable operating in ambiguous environments and at a rapid pace.
Low-ego, collaborative, and mission-driven, focused on team and company success.

Nice To Haves

Experience in AI/ML data operations or large-scale operations.
Background in audio, speech, or language-related workflows.
Background in Computer Science, Industrial Engineering, or a similar field.

Responsibilities

Partner tightly with research and modeling teams to identify model failures through evaluations and direct customer conversations.
Design, source, and maintain evaluations that define model quality, grounding them in observed customer failures and continuously benchmarking against other systems.
Translate evaluation results into concrete data requirements.
Establish new sources of human data to advance model performance by building collection pipelines from scratch.
Own data quality from acquisition through delivery.
Stay informed about the latest research by reading papers and understanding dataset impacts on model capabilities.
Collaborate with the team on future data collection strategies.
Engage with customers and enable the Go-To-Market (GTM) team to drive revenue growth.