Applied Scientist II

Microsoft•Redmond, WA

56d

About The Position

As an Applied Scientist - AI for Business & Industry Copilot Foundations team, you will play a pivotal role in advancing Microsoft's mission. You will contribute to the development and integration of cutting-edge AI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful. You will collaborate across product, research and engineering teams to bring innovative solutions to life, applying your expertise in machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experience. Microsoft is driving innovation in Artificial Inteligence (AI) and building an open architecture platform that empowers users with tailored AI for real-world impact. We are seeking an Applied Scientist - AI who combines expertise in AI and applied science, embraces a growth mindset, and demonstrates customer empathy to help shape the future of AI. 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 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.
1+ years of experience working with generative artificial intelligence or large language model and machine learning algorithms.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Nice To Haves

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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 3+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
OR equivalent experience.
3+ years of experience developing and deploying live production systems
2+ years of experience working with Generative AI models and Machine Learning (ML) stacks
1+ years experience creating publications (e.g., patents, peer-reviewed academic papers).
1+ years of experience conducting research in academic or industry settings
Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.
Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow)
Experience across the product lifecycle from ideation to shipping

Responsibilities

Build collaborative relationships with product and business groups to deliver AI-driven impact
Research and implement state-of-the-art using foundation models, prompt engineering, graphs, multi architectures, as well as classical machine learning techniques.
Fine-tune foundation models using domain-specific datasets. Evaluate model behavior on relevance, bias, hallucination, and response quality via offline evaluations, shadow experiments, online experiments, and Retun On Investment(ROI) analysis.
Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, support MLOps/AIOps.
Ability to use data to identify gaps in AI quality, uncover insights and implement PoCs to show proof of concepts.

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