Data Scientist II

Data Scientist II

Overview


Responsibilities

As a Data Scientist II within the AI Engineering team, you will:

Problem Solving & Stakeholder Collaboration

• Work with business stakeholders, Solution Managers, TPMs, and engineering teams to understand business context and translate defined problems into analytical and technical approaches.
• Contribute to scoping discussions, surfacing trade-offs and helping refine the path from business need to AI solution.

Technical Execution

• Build and deploy production AI/ML systems (ML/DL, GenAI, optimization) using Azure AI, open-source frameworks, and custom models, with ownership of model development from prototype through deployment.
• Apply engineering best practices for code quality, MLOps, testing, and monitoring; contribute to improving team-wide standards.

Continuous Learning & Contribution

• Stay current with emerging AI tools and frameworks; evaluate and apply them to your work.
• Share learnings with the team; collaborate openly and contribute to a rigorous, inclusive engineering culture.

Qualifications

Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience.
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results).
  • OR equivalent experience.

Preferred Qualifications:

• Hands-on experience with Azure-native platforms: Fabric, Kusto, Databricks, Synapse, Azure ML Studio.
• Exposure to supply chain, manufacturing, or hardware operations contexts (demand planning, yield optimization, logistics) is a plus.
• Experience contributing to MLOps infrastructure: CI/CD for models, automated retraining, drift detection.
• Familiarity with optimization methods (linear/mixed-integer programming, simulation) applied to operational problems.
• Systems thinking - ability to reason about trade-offs between model performance, engineering effort, and operational simplicity.

 

#W+DJOBS

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.

Experience Level

Mid Level