Senior Applied Scientist

Research and evaluate tools, technologies, and methods from the community that can improve the quality, performance, or efficiency of M365 Copilot Chat and Search, and apply them to deliver business impact. Build deep knowledge of the M365 Copilot Search service while staying current with industry trends and advances in applied ML. Consult with engineers and product teams to apply advanced concepts to search quality improvements. Mine data to identify opportunities to apply state-of-the-art algorithms that improve M365 Copilot search quality. Apply statistical analysis to map user journeys, evaluate the behavior of deployed models, validate assumptions about evaluation results, and communicate insights to the team. Develop expertise in search relevance, NLP, and data-driven analysis, along with the relevant research literature and techniques. Use this understanding to identify, adapt, or create research-backed solutions—novel, data-driven, scalable, and extensible—that improve M365 Copilot Chat and Search quality. This may include authoring papers, developing and maintaining internal tools and services, and publishing research. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ year(s) related experience OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Experience applying machine learning, NLP, or information retrieval techniques to search or recommendation problems.

using data analysis and offline/online evaluation to diagnose metrics, user journeys, model behavior and improve product quality.

Applied ML engineering experience using Python and modern ML frameworks such as PyTorch.

Hands-on experience with dense/vector retrieval and text embeddings, including bi-encoders, approximate nearest-neighbor search, contrastive training, or hard-negative training.

Familiarity with information retrieval fundamentals and retrieval evaluation, including metrics such as recall@k and nDCG, offline evaluation pipelines, and interpretation of A/B test results.