Principal Applied Science Manager Excel Team United States Washington Redmond

Job description

Lead, grow, mentor, and inspire an inclusive, high-performing team of applied scientists. Support individual career development and guide team members through complex technical and product problem spaces. Maintain a hands-on, builder-oriented approach, contributing directly to technical design, prototyping, experimentation, implementation, and production readiness as needed. Own the end-to-end technical and scientific direction for your team's AI capabilities, from identifying customer scenarios and defining success criteria through experimentation, evaluation, deployment, and continuous improvement. Translate product scenarios and customer needs into well-defined machine learning, LLM, retrieval, program synthesis, and agentic-system problems; identify key technical challenges and design experiments to address them. Drive projects from concept through implementation, experimentation, production integration, and successful release to customers. Establish and maintain a high scientific and engineering bar for model quality, reliability, safety, latency, cost, and customer value. Design and drive rigorous evaluation frameworks for intelligent applications, using offline metrics, human evaluation, telemetry, real-world feedback, and failure analysis to identify opportunities for improvement. Work directly with customers and partners to understand real-world usage patterns, pain points, and failure modes, translating those insights into product, scientific, and engineering priorities. Lead the preparation, curation, and quality assessment of datasets used for modeling, evaluation, and experimentation, including identifying data quality constraints and opportunities. Stay current on relevant research, industry trends, and emerging techniques, applying them pragmatically to customer-facing Excel experiences. 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). OR equivalent experience. 1+ year(s) of people management experience. These requirements include but are not limited to the following specialized security screenings: 4+ years of experience applying machine learning techniques and driving end-to-end AI product development from concept to shipping 3+ years of experience working with large language models. 3+ years of experience as a manager of a successful and impactful data science/applied science team with 5 or more direct reports. Demonstrated experience leading complex technical projects from problem definition and experimentation through production deployment and iteration. Experience mentoring, leading, or managing applied scientists, data scientists, machine learning engineers, or other technical contributors. Ability to work effectively across engineering, product management, design, and research partners in a fast-paced and ambiguous environment. Ability to explain technical tradeoffs, evaluation results, and product implications to a broad set of stakeholders. Demonstrated ability to lead end-to-end work in challenging technical domains, including planning, design, execution, continuous release, and service operation. Experience shipping internet-scale, low-latency, highly available intelligent systems. Experience applying foundation models, including domain adaptation, fine-tuning, and evaluation of LLMs or small language models. Experience with prompt optimization, retrieval-augmented generation, data mining using language models, and agentic systems. Experience with AI evaluation, including benchmark design, human evaluation, model-quality analysis, safety assessment, and telemetry-driven iteration.