Data Scientist - Capacity Planning - Apple Data Platform

Data Scientist
- Capacity Planning
- Apple Data Platform

Cupertino, California, United States Software and Services

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.

• Develop short- and long-range capacity forecasts for GPU, TPU, CPU, storage, and other infrastructure resources supporting large-scale data and AI workloads.
• Build demand forecasting models using historical utilization, workload growth, product roadmaps, seasonality, migrations, and engineering inputs.
• Translate workload forecasts into infrastructure requirements and actionable capacity plans.
• Develop analytical models for utilization, capacity efficiency, supply-demand gaps, and stranded capacity.
• Build infrastructure unit-economics models connecting capacity, performance, utilization, and cost.
• Evaluate trade-offs across on-demand, committed, reserved, dedicated, spot, and internally owned capacity.
• Develop scenario and sensitivity analyses to understand the impact of demand uncertainty, hardware changes, infrastructure commitments, and major platform transitions.
• Establish feedback loops and validation methods that compare forecasts with actual demand and continuously improve model accuracy.
• Partner with engineering teams to understand workload behavior, SLOs, architecture changes, and their impact on capacity requirements.
• Work closely with CIBO, Finance, and Procurement to support infrastructure investment decisions and long-range capacity planning.
• Identify opportunities to improve utilization, rebalance capacity, reduce stranded resources, and optimize infrastructure spend.
• Establish common metrics, assumptions, and forecasting methodologies across organizations.
• Automate capacity planning and reporting using data pipelines, models, and dashboards.
• Communicate analytical findings, risks, and recommendations clearly to engineering and business leaders.
• Help evolve capacity planning from reactive reporting into a forward-looking, data-driven decision capability for Apple Data Platform.

• 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.
• 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,…