Principal Applied Scientist

Full Job Description

Designing and training relevance models, including LLM fine-tuning and learning-to-rank (LTR) approaches Building robust evaluation pipelines using offline metrics and online A/B experimentation Drive end-to-end applied science projects: From ideation and design to implementation, experimentation, and shipping, you will lead high-impact projects that directly improve Copilot Chat, Copilot Search, and BizChat experiences. This includes identifying search and relevance gaps, formulating innovative hypotheses, and delivering scalable solutions. Inspire help to grow a high-performing applied science team: mentor, and empower a team of applied scientists-owning their technical direction, project execution, and career development. You will guide day-to-day work, ensure scientific and engineering rigor, and be accountable for the team's output and impact. Innovate with scientific rigor: Invent and apply cutting-edge techniques in machine learning, natural language processing, and information retrieval to address real-world challenges at enterprise scale. You will design novel approaches for improving retrieval, ranking, query understanding, and semantic search in Copilot systems. Document, share, and amplify learnings: Promote a culture of transparency and innovation by capturing experimental results, documenting methodology, and publishing internal learnings. You'll drive knowledge sharing that enables broader impact across the organization. Translate business goals into scientific strategy: Partner closely with product and business stakeholders to align team efforts with high-priority objectives. You will translate ambiguous product requirements into clear, data-driven, and technically feasible directions. Collaborate across organizations and time zones: Work cross-functionally with platform engineering teams, peer science orgs, and product managers to ensure alignment, resolve dependencies, and unblock progress. You'll be a key bridge between applied science innovation and product delivery. Embody our Culture and Values. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) These requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 10+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 5+ years experience in applied science projects from ideation to production-in high-scale environments such as search, recommendation, or conversational AI. 5+ years experience in data analysis at scale, including working with logs, telemetry, and large datasets to uncover behavioral patterns, build evaluation datasets, and derive insights. 6+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers). 2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker. 5+ years experience conducting research as part of a research program (in academic or industry settings). 5+ years experience developing and deploying live production systems, as part of a product team. 5+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping. Experience in search and ranking systems, semantic retrieval, and information retrieval at scale. Applied experience with state-of-the-art sparse and dense retrieval and ranking techniques. Experience with RAG (Retrieval-Augmented Generation) architectures using LLMs such as OpenAI GPT, T5, or Llama. Experience with Fine-tuning or prompt engineering of large-scale language models for query rewriting, summarization, and document reranking. Familiarity with hybrid search strategies, learning-to-rank (LTR) frameworks, and evaluation methodologies for IR systems (offline metrics, A/B testing, relevance judgments).

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