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 return on investment (ROI).
We are looking for a Marketing Data Scientist - Event Analytics who thrives at the intersection of events intelligence, causal measurement, and business impact - someone who can transform complex event and multichannel data into rigorous models that quantify how events influence marketing and business outcomes. Are you passionate about causal measurement, applied modeling, and understanding how marketing events actually move the business? Would you like to build the models and measurement approaches that partner with some of the world's most innovative event marketers and demand generation leaders, all whilst working in the dynamic, data-rich world of global-scale events supporting cloud-based business products? As a Marketing Data Scientist - Event Analytics in E&E, you will be a key modeler and hands-on contributor for events measurement across Microsoft's marketing ecosystem, from flagship events like Microsoft Ignite, Build, and AI Tour to regional roadshows, virtual, and hybrid formats. You'll build the modeling layer that estimates the incremental impact of events on downstream business outcomes (such as engagement, demand, and pipeline), connecting event registration and engagement data with customer relationship management (CRM), sales, and multichannel demand generation signals to isolate the impact of events. You'll design causal and statistical frameworks, accounting for confounding, selection bias, and attendee self-selection, and translate the results into executive-ready insights that help Event Marketing, Brand, product marketing management (PMM,) Customer Insights, and demand generation leaders make faster, better-informed decisions. We are seeking someone who is curious, comfortable with ambiguity, and able to use rigorous causal and statistical techniques to develop scalable, trustworthy event measurement. You build on the successes of others, value cross-team collaboration, 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
Event Impact Modeling & Causal Measurement
Help build and maintain the modeling framework that quantifies the impact of Microsoft marketing events, including flagship global events (Ignite, Build, AI Tour), regional experiences, virtual events, hybrid formats, and industry conferences
Design and apply causal inference approaches to estimate the true impact of events in settings where randomized experiments may be limited or infeasible
Connect event participation and engagement signals to CRM data to estimate event contribution
Establish standardized, defensible measurement methodologies and benchmarks for event impact across different event formats, scales, and business objectives
Modeling, Statistics & Analysis
Apply advanced statistical and machine learning techniques (e.g., regression modeling, propensity scoring, survival analysis, cohort analysis, time-series forecasting, uplift modeling) with a bias toward interpretability and causal validity over purely predictive performance
Balance methodological rigor with pragmatism, selecting approaches that are fit for purpose given observational, incomplete, or biased marketing data
Write high-quality analytical code (Python, SQL) to support reproducible research, exploratory analysis, and ongoing measurement efforts
Develop and support experimentation frameworks (A/B tests, holdouts, geo-based designs) to strengthen causal claims and rigorously test event marketing tactics, channel strategies, and audience targeting approaches
Demand Generation & Multichannel Attribution
Build and refine attribution and contribution models that connect demand generation activities - email campaigns, paid media, social promotions, owned content, seller outreach - to event registrations, attendance, and downstream outcomes
Integrate and normalize data across web analytics, marketing automation, event registration systems, email platforms, paid media tools, and CRM/sales systems to create a unified, analysis-ready view
Analyze channel effectiveness, funnel performance, and audience journey patterns to identify which channels and tactics drive the highest-quality business outcomes
Develop frameworks that relate event and channel investment to opportunity, pipeline, and revenue outcomes by segment and campaign
Data Understanding, Enablement & Stewardship
Prepare, validate, and analyze complex event, web, marketing automation, and sales datasets, identifying data quality issues, structural changes, and limitations
Communicate data risks, constraints, and appropriate caveats proactively to senior partners, ensuring conclusions are appropriately scoped
Help productize models and measurement into repeatable outputs and self-service dashboards (e.g., Power BI) that democratize access to event impact insights across Event Marketing, Demand Generation, PMM, Brand, and regional teams
Collaborate with Data Services and Data Engineering to ensure event, web, marketing automation, and sales data are accurate, integrated, compliant, and ready for analytical use, upholding high standards for data ethics, privacy, and responsible use
Other
Embody our Culture (https://www.microsoft.com/en-us/about/corporate-values) and Values (https://careers.microsoft.com/us/en/culture)
Qualifications
Required/minimum 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) experience in data science, statistical modeling, or marketing/measurement analytics
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years experience in data science, statistical modeling, or marketing/measurement analytics
OR equivalent experience.
Additional or preferred qualifications
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) experience in data science, statistical modeling, or marketing/measurement analytics
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years experience in data science, statistical modeling, or marketing/measurement analytics
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years experience in data science, statistical modeling, or marketing/measurement analytics
OR equivalent experience.
Experience building machine learning or statistical models to measure business impact, including causal or quasi-experimental methods
Experience applying statistical techniques to marketing data, such as regression modeling, propensity scoring, cohort analysis, survival analysis, A/B testing, and time-series forecasting
Experience analyzing business to business (B2B) marketing and event data from multiple disparate sources, including marketing attribution methodologies
Experience connecting marketing engagement data to CRM/sales pipeline systems to measure opportunity and pipeline impact
Experience with SQL, Python or R for statistical analysis and modeling, and data visualization tools such as Power BI, Tableau, or Looker -
Experience with Microsoft data products such as Fabric, Azure Data Explorer, Azure Synapse, and Kusto Query Language (KQL)
Experience with event technology platforms and large-scale flagship or multi-event programs
Data Science IC3 - The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 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 $133,800 - $219,200 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, read more about requesting accommodations. (https://careers.microsoft.com/v2/global/en/accessibility.html)
