Data Science Intern

Zillow•Seattle, WA

2d•$104,000 - $166,000•Hybrid

About The Position

Zillow’s Data Science and Analytics organization uses complex data to inform decisions across the company. The Data Science Intern will contribute to a scoped, real-world project, applying quantitative and programming skills while working with data scientists and cross-functional partners. With guidance from a manager and team, the intern will work through the analytical process: defining questions, preparing data, interpreting results, and sharing recommendations. The nine-week summer internship runs June 14 through August 13, 2027. Interns gain hands-on experience, connect with mentors and leaders, and participate in learning and community experiences with their cohort. Intern projects may support business, marketing, product, pricing, or data-tools teams. The position is based in Seattle and follows a hybrid schedule, with interns generally working in person Tuesday through Thursday. Eligible interns receive a stipend to help offset summer housing and relocation costs. This role is categorized as an Office position. Office employees regularly work at an existing ZG corporate office for approximately 80 to 100 percent of their time each month and must live within reasonable commuting distance of their designated office. The standard annual base pay range in California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC is $104,000 to $166,000. Actual amounts vary depending on experience, performance, and location; this range may not apply to other locations.

Requirements

Currently pursuing a bachelor's or master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Finance, or a related quantitative field.
Targeting a graduation date before December 2028.
Foundation in probability, statistics, and data analysis through coursework, research, or projects.
Ability to use Python or R, or a similar programming language, to manipulate data and perform analysis.
Familiarity with SQL and relational data, or demonstrated ability and willingness to learn.
Enjoys asking questions, investigating patterns, and bringing structure to ambiguous problems.
Thoughtful analytical approach, including checking assumptions, validating results, and distinguishing supported conclusions from uncertainty.
Ability to explain thinking clearly and collaborate with people from technical and nontechnical backgrounds.
Curiosity about using AI tools while maintaining independent judgment, validating outputs, and following data-handling guidelines.

Nice To Haves

Exposure to A/B testing, experimental design, or causal inference.
Experience with data visualization tools or libraries.
Familiarity with regression, predictive modeling, or other statistical learning methods.
Experience organizing reproducible work using notebooks, version control, or clear technical documentation.
Experience from coursework, academic research, student organizations, or personal projects is welcome; prior professional data science experience is not required.

Responsibilities

Partner with a manager and stakeholders to turn an open-ended business question into an analytical plan with clear goals and deliverables.
Use SQL and Python or R to explore, prepare, and validate data; document assumptions and flag data-quality issues.
Analyze customer behavior or business performance to uncover insights and potential opportunities.
Apply statistical methods such as regression, hypothesis testing, or experiment analysis, including A/B tests and pilots, as appropriate to the project.
Help define and interpret metrics connecting analyses to customer and business outcomes, and support ongoing performance tracking.
Create clear visualizations and reproducible analyses that make findings useful to others.
Collaborate with engineers, product managers, marketers, and other teammates on approaches to analyzing and communicating insights.
Communicate analytical approaches, results, recommendations, and limitations to technical and nontechnical audiences.
Document work and deliver a final presentation and handoff so the team can build on the findings.