Learning Analytics Data Scientist, Employee Experience & Productivity, IS&T
Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Oct 20, 2026. Apple scores A on the Alion truth index.
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 trains its employees and delivers knowledge resources. You will pull and prepare data from multiple systems, build dashboards and visualizations, and conduct analyses that help learning and development leaders understand program effectiveness and make informed decisions. 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 Knowledge & Training group by building and maintaining the analytical assets it relies on - dashboards, reports, and ad-hoc analyses that surface how training programs and knowledge resources are performing across the organization. You will draw on your expertise in learning and development analytics to understand how employees engage with training content, identify where programs can improve, and develop metrics that measure effectiveness beyond attendance. A core part of the role is partnership: you will work directly with knowledge and training leaders 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.
Minimum Qualifications
3+ years of experience working with learning and development data, including training platforms, content performance metrics, or learner engagement data.
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.
1+ years of experience in Python or R for data manipulation and analysis.
3+ years of experience delivering analytical work in a fast-paced environment with evolving priorities and multiple concurrent stakeholders.
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.
Preferred Qualifications
Working knowledge of how data informs content strategy, curriculum design, and self-service enablement.
Working knowledge of clickstream data and web analytics as applied to content performance and user engagement measurement.
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.
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)