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. 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. 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.