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

Apple•Austin, TX

14h

About The Position

Join Apple's Information Systems and Technology (IS&T) organization as a Product Data Scientist on the Employee Productivity & Support Data Science team. In this role, you will leverage data to enhance internal productivity tools and platforms. Your responsibilities will include extracting and preparing data from various systems, creating dashboards and visualizations, and performing analyses to help product leaders understand adoption, engagement, and overall portfolio performance. This position emphasizes interpreting data, deriving insights, and collaborating with business partners to translate these insights into informed decisions. You will support IS&T's Product group by developing and maintaining essential analytical assets such as dashboards, reports, and ad-hoc analyses to monitor the performance of Apple's internal productivity tools across the portfolio. Your expertise in product analytics will be crucial for understanding employee tool usage, identifying areas for experience improvement, and establishing metrics for adoption, engagement, and value delivery. A key aspect of this role involves close partnership with product leaders and program managers to grasp their challenges, formulate analytical approaches, and deliver clear, accurate, and actionable findings. You will act as a thought partner, possessing a strong business understanding to determine what metrics are significant and why. Daily tasks include writing SQL for data modeling from large datasets, building and maintaining Tableau dashboards, conducting trend and anomaly analyses, and presenting findings to various stakeholders, all while utilizing AI tools to boost analytical efficiency. The ideal candidate is technically proficient, detail-oriented, and driven by both the data itself and the business outcomes it enables.

Requirements

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.
3+ years of hands-on experience writing SQL to extract, transform, and analyze data from large, multi-source datasets.
3+ years of experience building dashboards and visualizations in Tableau, Looker, or equivalent.
3+ years of experience in Python or R for data manipulation and analysis.
3+ years of experience working with product analytics data, including feature adoption, user engagement, or platform utilization metrics.
3+ years of experience delivering analytical work in a fast-paced environment with evolving priorities and multiple concurrent stakeholders.

Nice To Haves

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.

Responsibilities

Build and maintain analytical assets (dashboards, reports, ad-hoc analyses) for the IS&T Product group.
Analyze how employees use internal tools to identify areas for experience improvement.
Develop metrics to measure adoption, engagement, and value delivery of internal tools.
Partner with product leaders and program managers to understand challenges and translate them into analytical work.
Deliver clear, accurate, and actionable findings to stakeholders.
Write SQL to model data from large datasets.
Build and maintain Tableau dashboards.
Conduct analyses to identify trends and anomalies.
Present findings to stakeholders at various levels.
Use AI tools to accelerate analytical work.