Senior Data Scientist, Elastic
Search, Agent Builder
CA$128.3-203k
This role is currently eligible to participate in Elastic's stock program
Open source platform powering search, observability, security, and more
Open for applications
Open source platform powering search, observability, security, and more
1001+ employees
Open for applications
CA$128.3-203k
This role is currently eligible to participate in Elastic's stock program
1001+ employees
Company mission
To help turn endless data possibilities into real results with the power of Search AI.
Role
Who you are
- 5–8 years in applied DS/ML with strong IR/NLP experience (RAG, dense/sparse retrieval, re-ranking, vector search)
- Proficiency in Python, PyTorch/Transformers, Pandas; reproducible experiments (e.g., MLflow), versioned datasets, and clean, reviewable code
- Hands-on evaluation expertise: offline metrics (nDCG/MRR/Recall@k), LLM-as-judge calibration, groundedness/citation scoring, and online A/B testing
- Experience turning experimental results into clear product calls (models, routing, tools) and communicating them crisply to cross-functional partners
- Practical Elasticsearch experience (or similar); ES|QL familiarity is a plus
- Comfort working in a distributed, async-first environment; strong written communication; low-ego collaboration
What the job involves
- The Search Conversational Experiences team builds Elastic’s new conversational (agentic) platform that lets customers chat with their own data in Elasticsearch
- We own the quality layer for RAG, agents and tools, retrieval/citations, streaming, memory, and—crucially—the evaluation signals that turn open-ended questions into grounded, reliable answers
- As a Senior Data Scientist, you’ll be part of a cross-functional team (backend, DS, PM, UX) driving chat quality end-to-end: designing and running evaluation pipelines, improving prompts and tool behaviors, and turning measurements into product decisions that customers can feel
- You’ll help tackle frontier problems—folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to boost groundedness, shaping agent routing and tool selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality and reasoning strategies where they truly move the needle
- This is an applied role: you will prototype, evaluate, and partner with engineers to ship
- Design and maintain offline/online evaluation pipelines for conversational search: golden sets, rubric/LLM-as-judge calibration, groundedness/citation checks, and A/B tests
- Build and compare retrieval & re-ranking baselines (sparse + dense), query understanding, and semantic rewrites; land improvements with clear metrics
- Use results to drive product decisions: model selection, efficient agent routing, tool gating, and agent customization for Elastic use cases in search and beyond
- Instrument dashboards and telemetry so helpfulness, faithfulness, latency, and cost trade-offs are visible and trustworthy; guard against regressions in CI
- Collaborate tightly with backend engineers on contracts (ES|QL, citations, telemetry), and with PM/UX to translate findings into shipped features
- Share outcomes clearly (docs, notebooks, PRs) and mentor peers in experiment design and evaluation craft
Salary benchmarks
Our take
Organizations are dealing with exploding data volumes increasingly sophisticated cyber threats, and the growing expectation tha ttechnology should help act on problems, not just report them. Ai is moving from experimentation into real operations, especially in security and observability where speed and context are critical. So now, both enterprises and governments are looking for simpler, shared platforms that sale easily and reduce complexity rather than add to it.
And Elastic sits at the intersection of these needs with its Search AI Platform that helps organizations turn large amounts of structured and unstructured data into clear insights and actiontions across search, security, and observability. Elastic's strength lies in its open, flexible approach, which breaks down data silos and provides a context people and AI systems need to make daster, better decisions. The goal is straightforward: help teams see what's happening, understand it quickly, and respond confidently.
Recently, the company worked with CISA to deliver a unified, cloud-based SIEM for US federal agencies, this highlights strong trust in Elastic's security and scalability. Alongside this, its AWS Agentic AI Specialization also reflects leadership in making autonomous AI system practical and reliable. Together, these milestones position Elastic well for the future, as organizations increasingly rely on shared platforms and intelligent systems to manage ris, complexity, and growth.
Freddie
Company Specialist at Welcome to the Jungle
Insights
8% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Jul 2016
$58m
SERIES D
Jun 2014
$70m
SERIES C
Total funding: $162m
Company 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).
Company HQ
Union Square, San Francisco, CA
Leadership
Ashutosh Kulkarni
(CEO)Ashutosh joined Elastic in 2021 as CPO, becoming CEO in 2022. Prior to this, he was an EVP and CPO of Enterprise at McAfee, a Senior VP and GM of Web Performance and Web Security at Akamai Technologies, and a Senior VP and GM of Informatica Cloud at Informatica.
Shay Banon
(Co-Founder & CTO)Shay co-founded Elastic in 2012 and has served as the CTO since.
Navam Welihinda
(CFO)Prior to joining Elastic in 2025, Navam was the CFO at Grammarly. He also spent nearly 8 years at HashiCorp as VP of Finance and CFO and was the Head of Finance, Compose at IBM for 1 year.
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