Apple is 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. The role aims to develop data products to improve Apple's software & hardware performance.
Requirements
- Strong mathematical foundations, software engineering, and broad knowledge of data analysis and practical machine learning are expected.
- 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)
- 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
Responsibilities
- 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.
- 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)
- Proficiency in distributed compute & storage technologies such as HDFS, S3, Iceberg, Spark, and Trino
Other
- Excellent machine learning, analytical, problem solving and communication skills.
- Ability to understand the broader business context, solve complex problems, and communicate findings effectively to stakeholders.
- 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.
- Experience driving cross-functional projects with diverse sets of stakeholders
