Microsoft · 1 week ago
Principal Applied Scientist
Redmond, WA
Full-time
Onsite
Senior Level, Lead/Staff
$140K/yr - $275K/yr
4+ years expMicrosoft is building a large scale, Azure-based intelligence platform to transform complex data into actionable insights for advertising stakeholders. As a Principal Applied Scientist, you will define the scientific vision and lead the development of machine learning and large language model components, ensuring robust models and guiding platform evolution toward automation and scalability.
Agentic AIApplication Performance ManagementArtificial Intelligence (AI)Business DevelopmentDevOpsInformation ServicesInformation TechnologyManagement Information SystemsNetwork SecuritySoftware
Responsibilities
Define and drive the modeling strategy for the advertising recommendations platform, spanning classical machine learning (for analytics on structured data) and the use of generative AI. You will set the direction on which problems to tackle with ML (e.g. predictive modeling, anomaly detection, clustering) and how to leverage LLMs to maximize user understanding and value
Architect end-to-end machine learning pipelines – oversee the design of data processing workflows, feature stores, model training/validation routines, and deployment mechanisms that can reliably produce daily insights for all customers. Ensure these pipelines are scalable, efficient, and maintainable, working closely with data engineering leaders on implementation
Lead the incorporation of LLM-based components for the platform’s intelligent narrative generation. This includes guiding the development of prompt frameworks, fine-tuning strategies, and retrieval-augmented techniques so that the system can answer complex sales questions and explain insights in conversational language
Oversee cross-team initiatives and collaboration, coordinating with engineering, program management, and stakeholder teams. You will chair technical design reviews, balance priorities, and guarantee that the data science efforts align with product requirements and timelines
Mentor and develop the applied science team, providing technical guidance to other scientists and engineers. Champion best practices in experimentation, coding, and MLOps, and foster a culture of scientific excellence and continuous learning
Ensure robust evaluation and governance of all AI/ML solutions. You will establish metrics for success (accuracy, precision of alerts, coverage of insights), closely monitor model performance in production, and implement processes for periodic retraining, validation, and Responsible AI compliance (addressing bias, fairness, and transparency)
Stay ahead of the curve by tracking emerging trends in AI, whether it’s new algorithms in anomaly detection or breakthroughs in large language models, and assess their potential to enhance the platform. Drive the incubation of innovative ideas, experimentally verify their benefits, and incorporate promising approaches to keep the platform technologically ahead and highly effective
Qualification
Required
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
Preferred
5+ years of experience with developing and deploying machine learning solutions in production, with proven ownership of complex projects end-to-end (from problem formulation and data acquisition to model deployment and monitoring)
3+ years of technical leadership experience in an applied science or data science team setting – this could include leading a team of scientists or acting as the key technical decision-maker on cross-discipline projects, with responsibility for delivering major features or systems
Extensive hands-on expertise in ML techniques for predictive analytics, pattern recognition, and optimization. You should be comfortable selecting and tuning algorithms for regression, classification, clustering, time-series forecasting, etc., and understand their trade-offs
Strategic thinking and excellent communication abilities – capable of translating high-level business objectives into technical plans and articulating complex AI concepts and project updates to senior leadership and non-technical stakeholders
Proficiency in programming and data infrastructure – solid coding skills in a programming language commonly used in ML (Python, etc.), experience with machine learning frameworks (e.g. PyTorch, Tensorflow), and familiarity with data pipelines and databases
Experience with natural language processing and LLMs – a deep understanding of how large language models can be applied, and practical experience either using pre-trained LLM APIs or training/fine-tuning NLP models for tasks such as summarization, question-answering, or conversational interfaces
Practical exposure applying LLMs to domain heavy contexts such as medical/health, farming/agriculture, social sciences, or related settings (e.g., domain adaptation, terminology grounding, retrieval augmented patterns) while adhering to privacy and Responsible AI expectations
Solid background in big data and cloud technologies – experience with Azure or similar cloud platforms for big data (Azure Synapse, Data Lake, etc.) and ML ops (Azure ML, MLflow), including building pipelines that handle streaming or real-time data for immediate insights
Proven track record of innovation and impact – for example, contributions to significant AI products or platforms, authorship of influential research publications or patents, or recognized leadership in the data science community
High proficiency in MLOps and AI governance – experience setting up automated training, implementing continuous monitoring and alerting for model performance, and ensuring models meet security, compliance, and ethical standards
Excellent cross-organizational leadership – ability to influence and drive alignment among teams with different priorities (engineering, sales, marketing, etc.), and to build consensus for ambitious technical initiatives that span multiple orgs or disciplines
Company
Microsoft
Microsoft is a software corporation that develops, manufactures, licenses, supports, and sells a range of software products and services.
10001+ employees
H1B Sponsorship
Microsoft has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (9192)
2024 (9343)
2023 (7677)
2022 (11403)
2021 (7210)
2020 (7852)
Funding
Current Stage
Public CompanyTotal Funding
$1MKey Investors
Technology Venture Investors
2022-12-09Post Ipo Equity
1986-03-13IPO
1981-09-01Series Unknown· $1M
Leadership Team
Satya Nadella
Chairman and CEO
Vukani Mngxati
Chief Executive Officer - Microsft South Africa
Recent News
2026-01-16
Morningstar.com
2026-01-16
Company data provided by crunchbase