Overview‍

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We are reimagining the marketing research discipline by building AI-powered tools that help researchers and data scientists move from question to insight faster. We’ve already delivered millions in cost savings for Microsoft and are scaling the next generation of intelligent, production-ready capabilities. In this role as a Data Scientist, you will design, build, and operate AI-enabled applications on top of our marketing research data ecosystem. You’ll partner closely with product, engineering, and research stakeholders to translate real user needs into secure, reliable experiences—bringing strong software engineering fundamentals (architecture, coding, testing, and deployment) plus applied AI expertise (LLMs, retrieval, evaluation, and monitoring). You’ll join a collaborative team that values clear problem statements, thoughtful trade-offs, and continuous learning. Our work spans the spectrum from rapid MVPs and prototypes to hardened, scaled production services—always with clear criteria for quality, security, and user impact. You will have room to focus deeply, iterate with users, and ship improvements end-to-end—from prototype to production—while measuring impact with well-defined quality and adoption metrics.

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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.  

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‍Responsibilities

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• Translate user and business needs into technical designs for AI-enabled features; write clear specs, define success metrics, and align on trade-offs with stakeholders.
• Design and build production services and application capabilities that integrate LLMs (prompting, tool use/agents, RAG, embeddings) with enterprise data.
• Implement end-to-end evaluation for features and iterate based on data, user feedback, and telemetry.
• Use proficient engineering judgment to choose the right approach for the moment (prototype/MVP vs. production), and define the “graduation” path: requirements, testing, evaluation, monitoring, and operational readiness.
• Own reliability in production: monitoring, alerting, incident response, performance optimization, and proactive risk mitigation.
• Build and maintain CI/CD, automated testing, and safe deployment practices for AI systems (including prompt/model configuration management where applicable).
• Partner with Responsible AI, security, and privacy stakeholders to ensure solutions are compliant, trustworthy, and designed for real-world use.
• Embody our culture and values.

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‍Qualifications‍

Required/minimum qualifications

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• 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., hands-on Python development, design and build production services and application capabilities that integrate LLMs, translate user and business needs into technical designs for AI-enabled features) 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., hands-on Python development, design and build production services and application capabilities that integrate LLMs, translate user and business needs into technical designs for AI-enabled features)
  • OR equivalent experience.

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Additional or preferred qualifications

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• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., hands-on Python development, design and build production services and application capabilities that integrate LLMs, translate user and business needs into technical designs for AI-enabled features)
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., hands-on Python development, design and build production services and application capabilities that integrate LLMs, translate user and business needs into technical designs for AI-enabled features)
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., hands-on Python development, design and build production services and application capabilities that integrate LLMs, translate user and business needs into technical designs for AI-enabled features)
  • OR equivalent experience.
• Hands-on experience building LLM-enabled applications (prompting, tool use/agents, RAG, embeddings) and integrating them with real data sources.
• Proficient software engineering fundamentals: data structures, APIs/services, code quality, testing, and debugging in distributed systems.
• Proficiency in Python and at least one of: C#, Java, Go, or TypeScript/JavaScript; working knowledge of SQL.
• Ability to communicate clearly with cross-functional partners and make pragmatic trade-offs based on constraints, risk, and impact.
• Experience with Azure and applied AI tooling (e.g., Azure ML, Azure OpenAI / OpenAI, PromptFlow, Databricks, Microsoft Fabric) and production CI/CD pipelines.
• Experience designing LLM evaluation approaches (golden sets, human-in-the-loop review, A/B tests), and building telemetry/quality dashboards.

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