Data Scientist, Employee Productivity & Support

Are you passionate about using data to improve the employee experience? Join Apple's Information Systems and Technology group, the engine powering our global operations. As a Data Scientist on the Employee Productivity & Support (EPS) team, you'll leverage advanced analytics to enable data-driven decisions that impact how Apple employees do their best work. You'll uncover insights from support data, ticketing systems, app usage, and operational processes, helping us optimize the IT ecosystem and create a more seamless and productive environment. You'll build tools that empower employees to independently solve complex problems, focusing on delivering exceptional experiences.

Description

In this role, you'll own the full analytics lifecycle, collaborating with IT support, product, engineering, and operations teams. You'll dive deep into support data from various channels (support apps, chat, phone, email, Slack) to understand how employees seek help, identify areas for improvement, and develop key metrics that measure operational efficiency and employee satisfaction.","responsibilities":"Apply your expertise in data wrangling and preparation to extract, clean, transform, and validate data from multiple systems, creating reliable datasets for analysis, modeling, and visualization.

Use causal inference techniques on observational data to mitigate confounding and bias, generating robust insights that support sound decision-making.

Develop models and forecasts to predict ticket volumes, staffing needs, and performance trends, enabling proactive IT support resource planning.

Integrate Gen AI tools, such as large language models, to summarize support patterns, classify tickets, and model sentiment, enhancing insight generation and responsiveness.

Maintain well-documented codebases in GitHub, deliver reproducible analyses, and mentor colleagues in standard processes.

Communicate your findings clearly to technical and non-technical audiences, improving impact, strengthening data literacy, and fostering a data-driven culture.

Preferred Qualifications

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

Deep understanding of statistical modeling and causal inference, including experimental and observational analysis, hypothesis testing, and measurement design.

Strong machine learning skills, including regression, classification, clustering, time-series forecasting, NLP, and unsupervised learning.

Advanced data wrangling and preparation skills, with experience extracting, cleaning, joining, and validating data from various sources to develop analysis-ready datasets.

Experience building reproducible pipelines with version-controlled analyses, well-documented methodologies, and reusable workflows.

Excellent written and verbal communication skills, with the ability to present complex information clearly to technical and non-technical audiences.

A strong dedication to documentation, ensuring collaboration and reproducibility.

Experience with IT support analytics, including working with ticketing data, support journeys, and multi-channel interactions (Slack, email, phone, chat).