Product Data Scentist, Employee Experience & Productivity, IS&T

The difference between data and intelligence is context. This role enriches data with business context to produce insights that enable intelligent decisions. Join Apple's Information Systems and Technology (IS&T) organization, the engine powering Apple. As a member of the Employee Productivity & Support Data Science team, you will use data to improve how Apple's internal productivity tools and platforms evolve. You will pull and prepare data from multiple systems, build dashboards and visualizations, and conduct analyses that help product leaders understand adoption, engagement, and portfolio performance. This role is about interpreting data, extracting insights, and partnering closely with the business to turn those insights into better decisions.

Description

In this role, you will support IS&T's Product group by building and maintaining the analytical assets it relies on - dashboards, reports, and ad-hoc analyses that surface how Apple's internal productivity tools are performing across the portfolio. You will draw on your expertise in product analytics to understand how employees use internal tools, identify where the experience can improve, and develop metrics that measure adoption, engagement, and value delivery. A core part of the role is partnership: you will work directly with product leaders and program managers to understand their challenges, translate them into analytical work, and deliver findings that are clear, accurate, and actionable. You are not building in isolation - you are a thought partner who understands the business well enough to know what to measure and why it matters. Day to day, you will write SQL to model data from large datasets, build and maintain Tableau dashboards, conduct analyses to identify trends and anomalies, and present your findings to stakeholders at various levels - all while using AI tools to accelerate your analytical work. The ideal candidate is someone who is technically strong, detail-oriented, and equally motivated by the work the data enables as by the data itself.

Responsibilities:

Partner with product leaders and program managers to understand their needs, translate business questions into data problems, and deliver recommendations grounded in evidence.

Extract, clean, transform, and validate data from multiple product and platform systems - creating reliable datasets for analysis and visualization.

Build and maintain dashboards and reports in Tableau, and actively consume those assets to identify trends, surface anomalies, and communicate insights back to stakeholders.

Conduct deep-dive analyses on product usage and adoption data to answer questions that existing dashboards do not address - packaging findings into clear, audience-ready deliverables such as slides or annotated visualizations.

Use AI to accelerate insight generation - summarizing patterns, classifying data, and enhancing the speed and depth of analytical work - while validating outputs to ensure accuracy and reliability.

Develop and maintain well-documented, reproducible analytical workflows - writing clean SQL, organizing code in GitHub, and ensuring that analyses can be understood and extended by others on the team.

Monitor data quality across product data sources, flagging issues proactively and working with data engineering to resolve them.

Preferred Qualifications

Master's degree or PhD in a quantitative or business field (e.g., statistics, data science, economics, operations research, applied mathematics, the natural sciences, or an MBA with analytics concentration)

Working knowledge of product management processes and how data informs roadmap prioritization, investment decisions, and portfolio rationalization.

Working knowledge of clickstream data, A/B testing frameworks, or feature flagging systems as applied to product decisions.

Proficiency in version control and collaborative documentation practices using tools like GitHub.

Knowledge of statistical modeling, including hypothesis testing, regression, and foundational causal inference methods.

Track record of applying AI to real-world data analytics and general productivity challenges.

Ability to communicate analytical findings clearly to both technical and non-technical audiences, adapting depth and format to the situation.

Comfort with ambiguity; ability to define the analytical approach when the business question is loosely framed.

Proven ability to build trust with leaders across diverse functional areas.

Minimum Qualifications

Bachelor's degree and 3+ years of relevant experience in data analytics, business intelligence, data science, or a related quantitative field - or a Master's degree in a quantitative or business discipline with 2+ years of relevant experience.