Role Description 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. - Work closely with backend engineering, product, UX, and other data scientists. - Lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base. - 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. - 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. Qualifications - 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. - Experience influencing product and technical strategy through data. - 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. - Experience collaborating closely with engineering teams to move from prototype to production. - Practical Elasticsearch experience, or experience with similar search and distributed data systems. - Excellent written and verbal communication skills. - A collaborative, low-ego style and a strong ability to mentor. 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. - Minimum of 16 weeks of parental leave.

Role Description 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 - 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 - 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. Qualifications - 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. Requirements - 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 - 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. - Increase your impact - 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.

Role Description - Kommunikation: Hauptansprechpartner für den Kundenkontakt deiner Kunden und die regelmäßige Kommunikation. - Performance-Analyse: Kontinuierliche Performance-Analyse und Ableitung proaktiver Maßnahmen, um Kundenbedürfnisse zu erkennen und die Kundenzufriedenheit zu sichern. - Kampagnenoptimierung: Verantwortung für die kontinuierliche Kampagnenoptimierung, um die Ziele der Kunden zu erreichen. - Umfang: Eigenständige Betreuung eines Portfolios von typischerweise 2 großen und 4 bis 5 mittelgroßen Kunden. - Creative Briefings: Erstellung präziser Briefings für Creative-Teams. - Umsatzverantwortung: Übernahme der Verantwortung für die Kundenbindung und Umsatzsteigerung als zentrale Schnittstelle zur Agentur. Qualifications - Meta Ads Expertise: Fundiertes und tiefes Wissen in Meta Ads als K.O.-Kriterium. - Erfahrung: Nachweisliche Erfahrung in der eigenständigen Betreuung von Kunden im Performance Marketing. - Growth Mindset: Stark ausgeprägtes Growth Mindset und Interesse an neuen Tools und Entwicklungen in der Branche. - Selbstorganisation: Hohe Fähigkeit zur Selbstorganisation und Eigeninitiative, um in unserer freien Agenturstruktur erfolgreich zu sein. - Bonus - TikTok: Kenntnisse im Bereich TikTok Ads. Benefits - Arbeitsort: 100% Remote-Work und flexible Arbeitsweise. - Workation: Wie z.B. Paris, Prag oder Gardasee. - Kultur: Flache Hierarchien und die Chance, Verantwortung schnell zu übernehmen und eigene Prozesse zu gestalten. - Entwicklung: Regelmäßige Feedbackschleifen zur aktiven Förderung deiner Entwicklung.

Role Description So wirst du Teil der Zukunft: Datenkompetenz leicht gemacht. Wir sind die DataSmart Point Academy, ein spezialisierter Weiterbildungsträger mit Fokus auf digitale Zukunftskompetenzen. Unser Ziel: Menschen praxisnah für Data, Analytics und Künstliche Intelligenz qualifizieren – flexibel, modern und anerkannt. Unsere Weiterbildungen bereiten dich gezielt auf eine Karriere in Data Science, Business Analytics und AI vor. In 6, 8 oder 12 Monaten lernst du alles, was du für deinen Einstieg brauchst – mit anerkanntem IHK-Abschluss und praktischen Skills in Excel, SQL, Power BI und Python. Alles 100 % online, praxisorientiert und flexibel – ideal für deinen beruflichen Neustart oder nächsten Karriereschritt. - Business Analytics (6 Monate) - Tools: Excel, SQL, Power BI - Fokus: Geschäftsprozesse verstehen, Daten analysieren, Entscheidungen optimieren - Für dich, wenn du Wirtschaft, Strategie, Vertrieb oder Marketing liebst – ohne gleich Programmierer*in zu werden. - Data Science & Data Engineering (8 Monate) - Tools: Python, Pandas, Statistik, Machine Learning, APIs - Fokus: KI-Modelle bauen, Prozesse automatisieren, tief in die Technik eintauchen - Für dich, wenn du Technik-Fan bist und gerne mit Code arbeitest. - Data Analyst Qualifizierung (12 Monate) - Tools: Excel, SQL, Power BI, Python, KI & Machine Learning - Fokus: Komplettausbildung – vom ersten Excelsheet bis zum finalen ML-Modell - Für dich, wenn du dir alle Türen offenhalten und ein ganzheitliches Skillset aufbauen willst. Qualifications - IHK-Zertifikat für deinen Lebenslauf - Microsoft Power BI für professionelle Datenvisualisierung - 100% online & flexibel – ideal berufsbegleitend - Praxisprojekte statt trockener Theorie Requirements - Leidenschaft für Daten & neue Technologien – du hast Lust, die Datenwelt zu entdecken - Interesse an Wirtschaft, Prozessen oder Technik – je nach Kursfokus - Motivation & Lernbereitschaft – wir starten bei den Basics und führen dich Schritt für Schritt in die Welt der Daten ein - Grundlegende PC-Kenntnisse (Office, Internet, E-Mail) - Keine Programmierkenntnisse erforderlich – wir bringen dir alles bei, was du für den Einstieg brauchst - Gute Deutsch- oder Englischkenntnisse - Optional hilfreich: Erste Erfahrung mit Excel, Zahlen oder Analysen - Freude daran, Neues auszuprobieren und dich stetig weiterzuentwickeln Benefits - Anerkannter IHK-Abschluss – wertvolles Zertifikat für deinen Lebenslauf - Praxisnah & 100% online – flexibel lernen, auch neben dem Job - Microsoft Power BI & SQL – gefragte Tools, die sofort im Job einsetzbar sind - KI & Machine Learning – moderne Skills für die Arbeitswelt von morgen - Individuelle Kurswahl (6, 8 oder 12 Monate) – je nach Karriereziel & Tempo - Begleitung durch Expert*innen – persönliches Feedback & Projektarbeit - Karrierestart in wenigen Monaten – statt jahrelangem Studium