Were looking for a Data Analyst to join the Data for AI team. This is a hands-on, customer-facing role focused on working with leading AI companies to turn real-world data into inputs that support model development and evaluation.
Youll collaborate closely with external AI teams and internal engineering and product partners to deliver data-driven solutions for specific AI use cases. The work is fast-paced, technical, and often open-ended, requiring comfort with large datasets, ambiguous requirements, and end-to-end ownership.
What does the day-to-day looks like:
Own end-to-end delivery of data solutions for AI use cases, from understanding model and product requirements to analysis, implementation, quality, and automation
Work hands-on with large, raw datasets to create high-quality data inputs that support model training, evaluation, and iteration
Apply strong quantitative analysis and data exploration skills to assess coverage, quality, and behavior of data used in AI systems
Build scripts, analyses, and reusable components in Python and SQL to support scalable and repeatable workflows
Collaborate closely with Engineering to ensure solutions are reliable, scalable, and production-ready
Partner directly with external AI teams and internal stakeholders to translate open-ended questions into concrete data outputs
Requirements: 4+ years of hands-on experience working with large-scale data using SQL and Spark or BigQuery
Strong Python skills for data analysis, scripting, and building reusable workflows
Experience working with raw, imperfect data and turning it into reliable, high-quality outputs
Strong analytical and problem-solving skills, with the ability to break down open-ended or ambiguous requirements
Ability to take end-to-end ownership of data projects, from exploration to delivery
Some hands-on experience with LLM-based systems, such as running inference via APIs, experimenting with prompts, or participating in basic evaluation or testing workflows
Clear communication skills in English and experience working directly with external stakeholders
Nice to have:
Deeper hands-on experience with LLMs in production or experimentation, for example prompt engineering, batch inference, or structured evaluation using APIs such as OpenAI, Anthropic, or similar providers
Familiarity with agent frameworks or orchestration layers (for example LangChain, LlamaIndex)
Experience with LLM evaluation or monitoring workflows, including offline evals, prompt regression testing, or tools such as LangSmith, Weights & Biases, TruLens, or Ragas
Experience experimenting with open-source or local models (for example via Ollama, vLLM, or Hugging Face tooling)
Familiarity with cloud-based data infrastructure, including AWS
This position is open to all candidates.