Data Scientist - Event Analytics
Microsoft (Eightfold Apply)
- Location
- United States, Washington, Redmond
- Work model
- On-Site
- Level
- Mid
- Posted
- 2h ago
Skills
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 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