Data Scientist - OS Program

About the position

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. The Project Analytics team, part of the Software Engineering OS Program, is seeking a data scientist to play a key role in developing new tools and processes. Every day, SWE engineers and leaders make critical decisions that drive product development throughout Apple. Come help us answer difficult questions, conduct investigations, and devise solutions that will inform decision-makers and empower engineers.

Responsibilities

• Identifying analysis opportunities from discussions with project management and engineering leadership.
• Performing exploratory data analysis to evaluate feasibility of investigations.
• Building data pipelines to extract data from Cassandra for transformation in Spark, and delivery to Tableau.
• Delivering analyses via self-service dashboards and in-person presentations with engineering leaders.
• Participate in periodic infrastructure support for our team’s Python-based, distributed micro services.

Requirements

• Worked with messy observational data to responsibly answer business questions within the limitations of the data.
• Used Git—or a similar tool—for managing and contributing to a versioned codebase.
• Have an understanding of continuous integration and unit testing concepts.
• Ability to write, debug, and review object-oriented Python code in a shared codebase.
• Fundamental understanding of probability and statistics principles.
• BS/MS or equivalent in Data Science, Computer Science, Statistics, Mathematics, etc.

Nice-to-haves

• Experience leading data investigations and analysis projects with ambiguous requirements.
• Developed and deployed analysis via a web app or dashboard with many asynchronous users.
• Used Apache Airflow to orchestrate ETL pipelines.
• Connected to Iceberg-formatted data tables using Trino Query Engine.
• Used Kubernetes to manage applications and/or distributed services on servers.
• Built or maintained database(s) in Cassandra, particularly with frequent writes or upserts.