Data Science: AI Experiences PhD Internship Opportunities - Redmond

Microsoft•Redmond, WA

4d•$6,810 - $13,480•Onsite

About The Position

Come apply your academic skills to solve real world industry problems at scale and do your best work at Microsoft! The complexity of the space and technical challenges of this opportunity will help you grow your skillset by collaborating with tenured PhDs across multiple disciplines (Computer Science, Statistics, Economics, etc.). Commerce, Content, and Monetization (CCM) is a 3,000+ person product, engineering, and data science organization where AI, content, commerce, and advertising converge. We build the technologies that power content and product discovery for billions of user interactions, helping people find what matters while driving a $10B+ business. From large-scale recommendation systems and AI-powered experiences to high-performance serving platforms, our teams tackle complex engineering challenges at extraordinary scale, processing millions of requests per second across billions of content and commerce signals. The solutions we build directly influence user experiences, advertiser success, and business growth. As a Data Scientist PhD Intern in CCM, you'll work alongside exceptional data scientists in diverse fields to solve meaningful problems with global reach and measurable impact. Some example problem areas may include personalized content and product discovery, recommendation, ranking, and search, and advertising and commerce measurement. You will use machine learning, statistics, controlled experiments, and/or AI-powered experiences, including LLMs, SLMs and agents to build and improve great products while making business impact. You will collaborate with stakeholders, formulate approaches to solve problems, and use algorithms and data sources in the context of business and engineering needs. You will also be able to combine your engineering abilities with analytical skills to make customer experiences better. 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 and 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. Please note this application is only for internships based in our Redmond, Washington office. For internships in other offices in the United States, please see our Careers site.

Requirements

Currently pursuing a Doctorate Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field.
Must have at least one additional quarter/semester of school remaining following the completion of the internship.
Candidate must be enrolled in a full time bachelor's, masters, MBA, or PhD program in area relevant for the role during the academic term immediately before their internship.

Nice To Haves

Some Engineering experience and or project course work using large data systems on SQL, Hadoop, etc.
Proficiency using one or more programming or scripting language to work with data such as: Python, Perl, or C#.
Some experience and or project course work performing data analysis and applying statistics working with tools such as: Excel, R, MATLAB, AMPL, or SAS.
Some experience and or project course work with product and service telemetry systems.
Some A/B Testing or experimentation (this can be from conducting real life science experiments, hypothesis testing in stats etc.) Not required but ideal.
Some experience or course work applying basic ML to a type of data and or used algorithms to conduct experiments on data.

Responsibilities

Own a scoped data science project from problem definition and metric selection through analysis, validation, and recommendation.
Analyze large-scale behavioral, content, commerce, and advertising data using SQL, Python, R, or comparable tools.
Design, execute, and interpret experiments, including defining success metrics, guardrails, and statistical limitations.
Apply appropriate methods—such as causal inference, statistical modeling, machine learning, or evaluation frameworks—to answer product and business questions.
Develop or improve metrics and monitoring approaches that reflect user experience and sustainable business value.
Partner with product managers, engineers, applied scientists, economists, and business stakeholders to identify opportunities and influence product decisions.
Communicate methods, findings, uncertainty, and recommendations clearly to technical and nontechnical audiences.
Participate in peer reviews, incorporate feedback, and document work so that results are reproducible and actionable.