About the Role
We’re 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.
You’ll 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
This role is a good fit if you have:
- 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