Principal Data Scientist (Agent Builder)
13 hours ago
Madrid
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: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel Define the evaluation strategy for conversational and agentic search, including offline and online evaluation, golden datasets, rubrics, LLM‑as‑judge calibration, groundedness and citation checks, and A/B testing Lead the design of quality metrics and decision frameworks for RAG, agents, tools, model selection, agent routing, prompt behavior, and cost/latency trade‑offs Build, compare, and guide improvements across retrieval and re‑ranking approaches, including sparse and dense retrieval, vector search, query understanding, semantic rewrites, and context enrichment Turn experimental results into product and business decisions: which models to use, how to route requests efficiently, which tools should be exposed, and how agents should be customized for different Elastic use cases Partner with engineering to productionize evaluation pipelines, telemetry, dashboards, CI guardrails, and regression detection for chat quality, helpfulness, dedication, latency, and cost Influence the roadmap by identifying the highest‑leverage quality gaps, proposing practical solutions, and communicating trade‑offs clearly to product, engineering, and leadership Mentor other data scientists and engineers in experiment design, evaluation methodology, statistical rigor, and practical approaches to improving LLM‑powered systems Share outcomes through clear docs, notebooks, PRs, dashboards, technical proposals, and cross‑functional reviews Benefits Toast to your health: Fully paid health coverage for you and your family, in many locations. Craft your calendar: Flexible location and schedule for most roles. Create space for you: Distributed by design workforce, plus generous number of vacation days each year. Embrace parenthood: Minimum of 16 weeks of parental leave, plus generous family formation benefits. Give back your time: 40 hours each year to use toward volunteering with organizations and causes you’re passionate about. Amplify your impact: Double your charitable giving — we match donations up to $1500 USD (or local currency equivalent). Strong track record defining and leading evaluation for production AI/ML systems, including offline metrics, online experimentation, LLM‑as‑judge approaches, groundedness, citation quality, and model comparison Practical Elasticsearch experience, or experience with similar search and distributed data systems. ES|QL familiarity is a plus Excellent written and verbal communication, with the ability to explain complex scientific and technical trade‑offs to engineering, product, design, and leadership audiences 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM‑powered product experiences Strong understanding of retrieval systems, including dense and sparse retrieval, re‑ranking, vector search, query understanding, and evaluation metrics such as n DCG, MRR, Recall@k, precision, and latency/cost trade‑offs A collaborative, low‑ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team Hands‑on ability with Python, Py Torch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking Experience influencing product and technical strategy through data, especially in ambiguous or emerging domains where the “right” metric or approach is not obvious at the start #J-18808-Ljbffr