Principal Data and Applied Scientist
About The Position
Microsoft’s Enterprise Engineering and AI (EEAI) Data Science and Applied AI team advances AI capabilities through evaluations, fine-tuning, and large-scale experimentation to create intelligent, personalized customer experiences. The team builds model evaluation frameworks, optimization pipelines, and experimentation platforms that support Microsoft’s AI ecosystem and company-wide AI transformation. The Principal Data and Applied Scientist will partner with engineering and product management groups responsible for Dynamics 365 applications used in Microsoft’s sales, marketing, and support platforms. The role applies advanced analytics, statistical modeling, machine learning, and generative AI to uncover insights, inform decisions, and deliver solutions to complex business challenges. The scientist collaborates with data scientists, engineers, and product managers and contributes to meaningful improvements for platform users.
Requirements
- Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field, plus 3 or more years of data-science experience; or a master’s degree in one of these fields plus 5 or more years of data-science experience; or a bachelor’s degree in one of these fields plus 7 or more years of data-science experience; or equivalent experience.
- Data-science experience that includes managing structured and unstructured data, applying statistical techniques, and reporting results.
- Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
Nice To Haves
- Experience with Python, PySpark, and SQL.
- Exposure to machine-learning development platforms such as Azure Machine Learning and Azure AI Foundry with Azure OpenAI.
- 6 or more years of experience in data science, product or journey analytics, causal inference, and user behavioral modeling.
- Experience driving product improvements through data and insights.
- Experience designing experiments, defining standardized metrics, performing causal analyses, and delivering behavior-driven insights.
- Experience with learning platforms and/or learner competency and skill modeling, including proficiency, mastery, and skill signals.
- Experience leveraging AI to accelerate time to insight and deepen insights.
- Experience with large-scale enterprise data platforms such as Fabric, Synapse, ADX, Delta Lake, ADF, Databricks, or Snowflake.
Responsibilities
- Work with stakeholders to understand business needs and formulate them as discrete, manageable problems with measurable objectives and outcomes.
- Turn formulated problems into implementation plans by defining success metrics, applying or creating appropriate methods, algorithms, and tools, and delivering statistically valid and reliable results.
- Write robust, reusable, and extensible code to support analysis and modeling.
- Develop machine-learning or generative-AI models using advanced statistical and machine-learning techniques.
- Lead evaluations of generative-AI solutions, diagnose issues, and identify root causes to support potential fine-tuning or reinforcement-learning fixes.
- Use AI-powered tools in daily work to accelerate coding, analysis, and other tasks.
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What This Job Offers
Job Type
Full-time
Career Level
Principal
