OverviewWe are looking for a Senior Data Scientist - Agentic Systems who thrives at the intersection of applied machine learning, large language models, and marketing operations. This is someone who can design, build, and ship generative AI capabilities that fundamentally change how an analytics team operates and how marketers consume insight.
As a Senior Data Scientist - Agentic Systems in E&E, you will be the technical lead for the team's generative AI work, spanning three areas: (1) the AI-native analytics operation - agents that act as co-PMs to our data scientists, manage hygiene and prioritization within Azure DevOps, and surface risks before they become escalations; (2) analyst delivery acceleration - internal large language model (LLM)-powered skills and workflows that compress the time from ask to insight, including automated generation of analytics inputs for monthly business review (MBR), and other leadership review rhythms; and (3) marketer-facing capabilities - most notably a conversational analytics agent that lets marketers self-serve on the questions they bring to us today.
You will both build and lead. Expect to spend meaningful time hands-on in the codebase - designing agent architectures, prototyping LLM skills, integrating against Azure DevOps and our analytics platforms, and shipping working capability into the team. You will also lead the vendor-developed portions of the work: scoping requirements, defining acceptance criteria, reviewing technical design, and holding partners accountable to quality and timeline. The role demands strong applied machine learning (ML) and software engineering judgment, the storytelling and partnership skills to bring marketing leaders along on what generative AI can and can't do for them, and the credibility to be a trusted technical partner in conversations across E&E and with engineering counterparts.
We are seeking someone who is curious, comfortable with ambiguity, opinionated about technical direction, and committed to shipping. You build on the work of others, value cross-team collaboration with data scientists, analysts, marketers, engineering partners, and vendors, and contribute to a diverse and inclusive workplace.
ResponsibilitiesAI-Native Operations- Design, build, and ship agentic capabilities that make the analytics team more AI-native, including co-PM agents that triage incoming work, monitor data science workstreams in Azure DevOps, and propose ticket updates that keep our backlog accurate without manual hygiene effort
- Build PM-layer agents that read across the Azure Dev Ops (ADO) portfolio to surface risk, estimate effort on new requests, and recommend project plans that managers can adapt rather than write from scratch
- Establish shared infrastructure and patterns - prompting, evaluation, orchestration, observability, guardrails - that let the rest of the team build downstream agents reliably
Analyst Delivery Acceleration- Develop LLM-powered internal tools and skills that compress the cycle from analytics request to delivered insight, including capabilities that draft, format, and pressure-test the standard inputs analysts produce for MBR, MMR, and leadership review rhythms
- Identify the highest-friction parts of the analyst delivery flow and design generative AI interventions that remove rather than relocate the work
- Partner with the analytics team to instrument adoption, measure time saved, and iterate based on real usage rather than projected value
Marketer-Facing Capabilities- Lead the design and delivery of marketer-facing generative AI capabilities, anchored by a conversational analytics agent that allows marketers across Brand, Product Marketing Management (PMM), Customer Insights, and demand generation to self-serve on the analytics questions they bring to the team today
- Define the data, retrieval, and grounding architecture required for marketer-facing agents to deliver accurate, sourced, and defensible answers
- Partner with marketers to drive adoption, working through trust, accuracy, and workflow fit - not just shipping the model
Vendor-Led Delivery- Lead the technical scoping, design review, and acceptance of vendor-developed work within the generative AI roadmap, ensuring partner-built capabilities meet quality, performance, and integration standards before they ship to internal users
- Translate strategic intent into requirements vendors can build against, and hold partners accountable to delivery commitments
Technical Leadership & Enablement- Serve as the team's go-to technical lead on generative AI, modeling strong applied ML quality, evaluation discipline, responsible AI practice, and engineering rigor
- Build shared patterns for prompt design, model evaluation, agent observability, cost management, and safety review that the team can reuse across projects
- Coach data scientists and analysts on generative AI patterns, helping the broader team grow its capability over time
Storytelling & Business Impact- Translate generative AI capability and limitation into language marketing leaders can act on, building the trust required for AI-mediated workflows to actually be adopted
- Partner with E&E leadership, product marketing, and engineering counterparts to communicate progress, surface blockers, and inform direction across the work
Other- Embody our Culture and Values
QualificationsRequired/minimum qualifications- Doctorate 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 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., 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 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
- OR equivalent experience
Additional or preferred 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.
- Experience designing, building, and shipping generative AI applications in production, including work with large language models, retrieval-augmented generation (RAG), and agentic system patterns (required)
- Experience in Python and the modern Python AI stack (e.g., frameworks for agent orchestration, evaluation, and LLM application development)
- Experience integrating LLM-based systems with enterprise data sources and operational platforms (e.g., work management systems, BI tools, marketing data ecosystems)
- Experience leading or anchoring the technical direction of multi-quarter generative AI initiatives, including those delivered in partnership with external development vendors
- Experience translating complex applied ML work into clear narratives that influence executive decision-making and shape investment direction
- Experience building production agentic systems with frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent
- Experience with Azure OpenAI, Azure AI Foundry, and the broader Microsoft AI platform stack
- Experience designing evaluation frameworks for LLM-based applications, including offline benchmarks, online metrics, and human-in-the-loop quality processes
- Experience in marketing analytics, business to business (B2B) technology marketing, or enterprise cloud business contexts
- Experience working with Microsoft data products such as Fabric, Azure Data Explorer, and Power BI
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800.00 - $234,700.00 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 $160,200.00 - $261,000.00 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
