Job description

Overview The Data Science & Applied AI (DSAAI) organization within Commerce Engineering & AI (CEAI) is leading Microsoft's transformation into an AI-native enterprise. Our mission is to combine advanced machine learning, Generative AI, agentic systems, and applied science to power intelligent experiences across Microsoft's commercial, sales, customer success, support, and business operations ecosystems. We are looking for a Principal Machine Learning Scientist to lead the development of next-generation AI systems, including large language models, AI agents, recommendation systems, predictive analytics, evaluation platforms, and AI-driven decision intelligence solutions. This role requires deep expertise in machine learning science, Generative AI, AI-native development methodologies, experimentation frameworks, and large-scale production systems. The successful candidate will drive technical strategy, influence engineering and product roadmaps, mentor scientists and engineers, and deliver measurable business impact across CEAI. 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. Responsibilities Key Responsibilities: • ML/AI Strategy & Technical Leadership: • Define and drive the scientific vision for AI and machine learning initiatives across CEAI. • Influence technical strategy and roadmap decisions across multiple product and platform teams. • Serve as a trusted advisor to engineering, product, and executive leadership. • Lead cross-organizational initiatives involving AI platforms, model development, evaluation systems, and business intelligence capabilities. • Mentor scientists and engineers while establishing scientific and engineering best practices. • Model Operationalization: • Implement CI/CD pipelines for ML models, ensuring smooth deployments with minimal downtime. • Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew. • Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations. • AI-Native AI/ML Development: • Design and develop AI-native experiences leveraging Large Language Models (LLMs), AI agents, copilots, retrieval systems, and autonomous workflows. • Lead development of agentic systems utilizing tool orchestration, memory architectures, reasoning frameworks, and multi-agent collaboration. • Drive adoption of AI-assisted and AI-native ML development practices. • Establish evaluation-driven development methodologies that continuously measure quality, relevance, usefulness, safety, and business impact. • Operational Excellence: • Partner with cross-functional teams, including Data Engineering, Product Management, and other Engineering teams to build cohesive solutions. • Provide technical guidance to engineers and drive best practices for MLOps within the team. • Partner with engineering teams to productionize ML/AI solutions at MCAPs at scale • Drive adoption of modern ML practices. • Security & Compliance: • Work on securing models, data pipelines, and infrastructure in compliance with Microsoft's security standards. • Ensure that the entire ML lifecycle adheres to privacy and compliance requirements (e.g., GDPR, CCPA). Qualifications Required/minimum qualifications Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Other Requirements 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. Preferred Qualifications • 10+ years developing and deploying machine learning solutions in production. • 5+ years leading large-scale AI or machine learning initiatives. • Experience delivering measurable business impact through applied science. • Experience in machine learning, deep learning, statistical modeling, and experimentation. • Experience with Large Language Models and Generative AI technologies. • Experience building production AI systems using Azure AI, Azure OpenAI, Azure Machine Learning, or similar platforms. • Proficiency in Python and modern ML frameworks including PyTorch, TensorFlow, Hugging Face, Semantic Kernel, AutoGen, LangGraph, or equivalent. • Experience designing and evaluating agentic AI systems and AI-native applications. • Experience with MLOps, LLMOps, model governance, and AI observability. • Experience building enterprise copilots, AI agents, or autonomous workflows at scale. • Experience with AI evaluation platforms, benchmarking systems, and model quality measurement frameworks. • Experience with Sales Intelligence, Commercial Operations, Customer Success, Commerce Systems, Business Applications, Revenue Optimization, Customer Insights • Experience patents, publications, open-source leadership, or notable technical innovation. • Experience leading AI transformation initiatives within large organizations. • AI-native operating models and human-AI collaboration patterns experience. #CEAIjobs Data Science IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: careers.microsoft.com This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Requirements

Must-haves

Define and drive the scientific vision for AI and machine learning initiatives across CEAI.
Influence technical strategy and roadmap decisions across multiple product and platform teams.
Serve as a trusted advisor to engineering, product, and executive leadership.
Lead cross-organizational initiatives involving AI platforms, model development, evaluation systems, and business intelligence capabilities.
Mentor scientists and engineers while establishing scientific and engineering best practices.
Implement CI/CD pipelines for ML models, ensuring smooth deployments with minimal downtime.
Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew.
Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations.
Design and develop AI-native experiences leveraging Large Language Models (LLMs), AI agents, copilots, retrieval systems, and autonomous workflows.
Lead development of agentic systems utilizing tool orchestration, memory architectures, reasoning frameworks, and multi-agent collaboration.
Drive adoption of AI-assisted and AI-native ML development practices.
Establish evaluation-driven development methodologies that continuously measure quality, relevance, usefulness, safety, and business impact.

Nice-to-haves

Experience delivering measurable business impact through applied science.
Experience in machine learning, deep learning, statistical modeling, and experimentation.
Experience with Large Language Models and Generative AI technologies.
Experience building production AI systems using Azure AI, Azure OpenAI, Azure Machine Learning, or similar platforms.
Proficiency in Python and modern ML frameworks including PyTorch, TensorFlow, Hugging Face, Semantic Kernel, AutoGen, LangGraph, or equivalent.
Experience designing and evaluating agentic AI systems and AI-native applications.
Experience with MLOps, LLMOps, model governance, and AI observability.
Experience building enterprise copilots, AI agents, or autonomous workflows at scale.
Experience with AI evaluation platforms, benchmarking systems, and model quality measurement frameworks.
Experience with Sales Intelligence, Commercial Operations, Customer Success, Commerce Systems, Business Applications, Revenue Optimization, Customer Insights
Experience patents, publications, open-source leadership, or notable technical innovation.
Experience leading AI transformation initiatives within large organizations.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlci/cdazurepython

Platform: eightfold
Effort: medium

Benefits

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Vision