Principal Data Scientist - Agent Builder

Elastic; Sweden; On-site; SEK 831,700 - 1,315,600; Full time

Benefits offered by this job

Competitive pay

Health coverage

Flexible locations and schedules

Generous vacation days

Job summary

PowerToFly in Sweden is seeking a Principal Data Scientist to spearhead evaluation strategies for our conversational experiences. The successful candidate will help set technical direction and mentor teams in enhancing chat quality, leveraging best practices in AI/ML.

With a focus on building resilient, insightful models, you will partner closely with product, engineering, and design teams to align product improvements with user needs, shaping the future of Elastic's agentic platform.

Qualifications

• 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences.
• Experience collaborating closely with engineering teams to move from prototype to production.
• Strong understanding of retrieval systems and evaluation metrics.

Responsibilities

• Define the evaluation strategy for conversational and agentic search.
• Lead the design of quality metrics and decision frameworks.
• Mentor other data scientists and engineers in experiment design.

Skills

Applied DS/ML experience

Expertise in IR and NLP

Hands-on Python and ML libraries

Elasticsearch experience

Strong communication skills

Education

Relevant degree in Data Science or related field

Tools

Python

PyTorch

Pandas

Job description

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: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel.

What You Will Be Doing:

• 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 productionise 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.

What You Bring:

• 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM‑powered product experiences.
• 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.
• 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.
• Hands‑on ability with Python, PyTorch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code.
• Strong understanding of retrieval systems, including dense and sparse retrieval, re‑ranking, vector search, query understanding, and evaluation metrics such as nDCG, MRR, Recall@k, precision, and latency/cost trade‑offs.
• Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking.
• 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.
• A collaborative, low‑ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team.

Benefits:

• Competitive pay based on the work you do here and not your previous salary
• Health coverage for you and your family in many locations
• Ability to craft your calendar with flexible locations and schedules for many roles
• Generous number of vacation days each year
• We match up to $2000 (or local currency equivalent) for financial donations and service
• Up to 40 hours each year to use toward volunteer projects you love
• Embracing parenthood with a minimum of 16 weeks of parental leave
• 401(k) / Retirement Plan
• Health coverage & vision insurance
• Flexible locations & schedules
• Work from home policy
• Generous vacation time & paid holidays

Security & Privacy Responsibilities: Take ownership of protecting the confidentiality, integrity, and availability of organizational data and systems by following applicable privacy and security policies, standards, and procedures. Ensure that all individual contributions follow Elastic’s Secure Software Development Framework (SSDF). Proactively participate in mandatory role‑based training to ensure personal technical execution consistently aligns with the highest standards of data protection, data privacy, and system resilience.

Elastic is an equal‑opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation.

We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email [e-mail]. We will reply to your request within 24 business hours of submission.

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Family and Medical Leave Act (FMLA) Poster

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Compensation for this role is in the form of base salary. This role does not have a variable compensation component.

At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets.

The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position.

The typical starting salary range for this role is:

831 700 kr—1 315 600 kr SEK