Elastic
Technology
PrincipalDataScientist-AgentBuilder
Neural analysis suggests this role is
optimal for Principal candidates.
“Principal Data Scientist - Agent Builder at Elastic. Skills: Data science, Machine learning, Data engineering, AI. Build and deploy machine learning models. Develop and maintain data pipelines”
Industry & Context.
Root cause analysis; Troubleshooting; Data-driven decision making
What You'll Do.
Build and deploy machine learning models
Develop and maintain data pipelines
Design and implement data solutions
Conduct data analysis
Perform statistical modeling
Collaborate with engineering teams
Collaborate with product teams
Drive data science initiatives
Contribute to data strategy
How You'll Work.
Team & Collaboration
Cross-functional teams; Engineering teams; Product teams
Full Job Description
Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Search Conversational Experiences team builds Elastic’s new conversational and agentic platform that lets customers chat with their own data in Elasticsearch. We build the core quality layer for RAG, agents and tools, retrieval and citations, streaming, memory, and the evaluation signals that turn open-ended questions into grounded, reliable answers. As a Principal Data Scientist, you will help set the technical direction for how we evaluate, improve, and scale chat quality across Elastic’s agentic platform. You will define the evaluation strategy that guides product decisions, including which models we standardize on, how we route requests across agents, which tools we enable and when, and how we tailor agents to different Elastic use cases in search and beyond. You will work closely with backend engineering, product, UX, and other data scientists to turn ambiguous, cutting-edge problems into measurable product improvements. You’ll help lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to improve groundedness, shaping reasoning strategies and tool-selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality where it can create meaningful user value. This is an applied leadership role
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