Data Engineer - Capacity Planning - Apple Data Platform

Help us build the capacity intelligence behind Apple’s next generation data and AI platforms. The Apple Data Platform organization is looking for a Data Scientist to help forecast infrastructure demand, optimize capacity, and improve the economics of large-scale compute environments.

You will work at the intersection of data science, infrastructure engineering, and finance, helping translate rapidly changing workload demand into actionable capacity plans and investment decisions. The role will initially focus on third-party cloud infrastructure and will expand over time to include Apple-owned infrastructure.

This is an opportunity to directly influence how Apple plans, allocates, and optimizes the infrastructure supporting some of its most important data and AI workloads.

As a Data Scientist focused on Capacity Planning within the Apple Data Platform organization, you will develop forecasting models, analytical frameworks, and data products that help teams make better infrastructure decisions.

You will partner closely with engineering teams to understand workload growth, product roadmaps, migrations, performance characteristics, and future infrastructure needs. You will also work with CIBO, Finance, Procurement, and infrastructure teams to evaluate capacity strategies, cloud commitments, and investment decisions.

Your initial scope will include GPUs, TPUs, compute, storage, and related resources across third-party cloud environments. Over time, you will help establish a unified capacity planning framework spanning both third-party and Apple-owned infrastructure.

3+ years of experience in data science, capacity planning, forecasting, infrastructure analytics, financial modeling, operations research, or a related quantitative field. Strong proficiency in SQL and experience analyzing large and complex datasets. Experience developing forecasting, statistical, scenario-analysis, or optimization models.

Strong analytical and quantitative problem-solving skills. Ability to translate ambiguous engineering or business problems into structured analytical approaches. Experience building dashboards, metrics, models, or data products that support operational or investment decisions.

Ability to communicate analytical findings and recommendations clearly to technical and non-technical stakeholders. Strong collaboration skills and experience working across engineering, finance, operations, procurement, or product organizations.

Proven experience in building highly scalable, compliant, and secure, enterprise-grade data and analytics platforms with robust data quality, data governance, data discovery, catalog and visualization capabilities. Proven experience in leveraging data for actionable insights.

Bachelors degree required in Business (with quantitative emphasis), Statistics, Data Mining, Machine Learning, Analytics, Econometrics, Mathematics, Operations Research, Industrial Engineering, or related field.

Ability to quickly build relationships and partner cross-functionally at all levels of the organization Familiarity with standard end-to-end financial processes such as forecast, close, analysis, and reporting Success leading multiple activities, projects, priorities, and people in a dynamic, fast-growing, and results-driven environment Ability to gain an understanding of the functionality of various systems Experience and knowledge in SQL, Python, and Tableau Outstanding attention-to-detail and organizational skills Experience with capacity planning for GPU, TPU, CPU, storage, or other large-scale compute environments.

Experience analyzing infrastructure capacity and cost across public cloud platforms such as AWS or GCP. Understanding of AI/ML infrastructure, accelerator utilization, training and inference workloads, and GPU/TPU capacity planning. Understanding of capacity metrics such as throughput, utilization efficiency, latency/SLO constraints, and hardware characteristics.

Experience with infrastructure unit economics, total cost of ownership, or cost-performance analysis. Experience managing uncertainty across committed and elastic capacity. Experience planning for major workload migrations or infrastructure transitions.

Experience with visualization and analytics tools such as Tableau or similar platforms. Experience building automated forecasting or capacity-planning pipelines. Familiarity with Apple Silicon, GPU, or TPU infrastructure.

Ability to operate effectively in a fast-moving environment where demand, technology, and infrastructure economics are continuously evolving.