Senior Machine Learning & Data Scientist, OS Power & Performance

Great performance is critical to Apple's product experience. We are seeking a Senior 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 Apple's software & hardware performance excites you, we encourage you to apply!

Description

We're 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.

Minimum Qualifications

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.

Preferred Qualifications

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 company's overall strategy and objectives.