Great performance is critical to Apples product experience. We are seeking a Machine Learning & Data Scientist to help with quantitative analysis of high dimensional data to draw insights that would impact hundreds of millions of users. If the idea of developing data products to improve Apples software & hardware performance excites you, we encourage you to apply! Were looking for a proactive & impact-driven engineer with excellent machine learning, analytical, problem solving and communication skills. In this role, you will analyze high dimensional data to derive meaningful insights and be responsible for producing metrics, models, simulations, and tools for analysis & communication of insights from large datasets. To be successful, you must have a strong foundation in statistical analysis and the ability to apply it to solving business & product-development problems, as well as a strong software engineering background with the ability to write production level code. As a member of this team, you will have the opportunity to provide meaningful insights to teams and influence decisions across Apple on a broad range of products. Analyze high dimensional data to derive meaningful insights. Produce metrics, models, simulations, and tools for analysis & communication of insights from large datasets. Apply statistical analysis to solving business & product-development problems. Write production level code. Provide meaningful insights to teams and influence decisions across Apple on a broad range of products. Strong Quantitative Foundation: Education in Computer Science, Electrical Engineering, or a related quantitative field. Strong mathematical foundations, software engineering, and broad knowledge of data analysis and practical machine learning are expected. Data Engineering and Analytics: Skilled at scalably transforming raw data into actionable insights through practical problem formulation followed by building of ETL processes (e.g. Python & Spark) and data visualizations (e.g. Tableau). Business Acumen and Problem-Solving: Ability to understand the broader business context, solve complex problems, and communicate findings effectively to stakeholders. Adaptability and Collaboration: Comfortable with ambiguity, eager to learn, and capable of working effectively in a collaborative environment. Strong interpersonal skills and the ability to build relationships with diverse stakeholders are essential. M.S. or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a similar quantitative field, with strong statistical skills and intuition. Proficiency in distributed compute & storage technologies such as HDFS, S3, Iceberg, Spark, and Trino. Proficiency with designing ETL flows and automation/scheduling (e.g. Kubernetes and Airflow). Working knowledge of Operating Systems. Experience driving cross-functional projects with diverse sets of stakeholders. * Skilled at connecting data insights to the companys overall strategy and objectives.
We’re a diverse collection of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. The people who work here have reinvented entire industries with the Mac, iPhone, iPad, and Apple Watch, as well as with services, including iTunes, the App Store, Apple Music, and Apple Pay. And the same passion for innovation that goes into our products also applies to our practices — strengthening our commitment to leave the world better than we found it.
There’s a place here for every kind of brilliant. Everyone here is an innovator, or an innovator-to-be, no matter what your team or your role. So bring your passion, courage, and original thinking and get ready to share it, because every new product, service, or feature we invent is the result of people working together to make each others’ ideas stronger. Innovation at this level depends on people who represent the variety of the human experience and inspire us with their own fresh perspectives. Together, we’ll do amazing work that can make a difference in people’s lives. Including your own. Learn more about working at Apple.