Principal Applied Scientist

Microsoft•Redmond, WA

1d•$188,000 - $304,200

About The Position

The AI Transformation team within Microsoft Frontier Company is reimagining how the company operates in the age of AI. We work across the business to transform critical end-to-end workflows, bringing people, AI agents, and systems together to create fundamentally new ways of working. Our team brings together engineering, applied AI, program leadership, and business transformation to move rapidly from opportunity to working solution. We operate with a builder mindset: hands-on, highly collaborative, comfortable with ambiguity, and biased toward action. Our goal is not simply to add AI to existing processes, but to build AI-native operating models and reusable solutions that improve how Microsoft Frontier Company works and accelerate transformation at scale. As a Principal Applied Scientist, you will apply data science, machine learning, and generative AI to build intelligent solutions that transform how Microsoft Frontier Company operates. You’ll work in small, multidisciplinary teams to experiment rapidly, evaluate approaches, and turn data and emerging AI capabilities into agents, models, and decision systems that deliver measurable operational impact. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Requirements

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)
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)
Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
equivalent experience.

Nice To Haves

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)
Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
equivalent experience.
2+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
3+ years experience conducting research as part of a research program (in academic or industry settings).
3+ years experience developing and deploying live production systems, as part of a product team.
3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

Responsibilities

Advance and apply state-of-the-art AI, including foundation models, reasoning systems, multi-agent architectures, RAG, and emerging techniques, to solve complex operational problems across Microsoft Frontier Company.
Identify high-value opportunities where applied AI can fundamentally improve how work is performed, and translate ambiguous business problems into rigorous technical approaches and experiments.
Rapidly prototype, evaluate, and iterate on AI solutions, using AI-first development and coding tools to accelerate experimentation and move promising approaches toward production.
Design robust evaluation methodologies, benchmarks, experiments, and success criteria that connect model and system performance to measurable operational outcomes.
Build scalable data, experimentation, and learning loops that use real-world signals and feedback to continuously improve AI systems.
Translate research into production-ready capabilities, partnering closely with engineering teams across architecture, implementation, deployment, observability, reliability, and performance.
Develop reusable AI methods, frameworks, and evaluation approaches that can accelerate multiple transformation scenarios rather than solving only a single use case.
Provide technical leadership across applied AI initiatives, helping teams evaluate emerging techniques, make sound technical tradeoffs, and determine where new approaches can create meaningful impact.
Ensure Responsible AI, privacy, security, scientific rigor, and appropriate governance are incorporated throughout experimentation and deployment.