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About This Role
Overview
The Global Marketing Engines and Experiences (E\&E) team within Microsoft is responsible for delivering integrated marketing experiences for Microsoft by building, running, and innovating a globally scaled engine to deliver connected journeys that delight customers and create fans. The Marketing Analytics and Data Science team within E\&E enables data\-driven decision making by providing data products and insights that measure marketing's performance and impact, deepen understanding of customer behavior, and drive marketing efficiency and ROI.
We 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.
Are you passionate about generative AI, agentic systems, and applying them to the day\-to\-day reality of how analytics organizations run? Would you like to help shape and build the generative AI roadmap for one of Microsoft's largest marketing analytics teams, working alongside data scientists, marketers, engineering partners, and external development vendors? And to do that in the dynamic, customer\-focused, and data\-rich world of cloud\-based business products?
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.
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.
In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.
Responsibilities AI\-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
Qualifications Required/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.
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.
Salary Context
This $119K-$234K range is above the median for Data Scientist roles in our dataset (median: $160K across 245 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Microsoft, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($177K) sits 10% below the category median. Disclosed range: $119K to $234K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Microsoft AI Hiring
Microsoft has 20 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, Data Scientist. Positions span New York, NY, US, Mountain View, CA, US, San Francisco, CA, US. Compensation range: $151K - $331K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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