**Role Number:** 200626394-3337

**Summary**

The Apple Data Platform powers analytics, machine learning, and critical decision-making systems across Apple. As the scale of our data and compute grows, cost efficiency and fiscal stewardship are vital to maintaining Apple’s culture of innovation and responsibility.

**Description**

We are seeking a Staff Data Scientist, Platform Economics to define the economic architecture of Apple’s Data Platform. In this role, you will treat infrastructure efficiency as a high-dimensional optimization problem—designing the data models, metrics, and telemetry pipelines that make resource usage visible, actionable, and intelligent. You will bridge the gap between complex distributed systems and strategic planning, building the algorithmic foundation that ensures every unit of compute delivers maximum business value. You will lead modeling efforts to right-size resources, leverage cost-saving pricing models (e.g., committed use discounts), and implement automated cost-control measures. This is a unique opportunity in a growing data science and platform economics team with a charter to optimize operations and planning with complex trade-offs between customer experience, cloud optimization, risk, and operational efficiencies.

**Minimum Qualifications**

+ Experience: 8+ years of experience in Data Science, Platform Engineering, or Systems Analytics, with a specific focus on infrastructure economics, scalability, or performance modeling.

+ Technical Proficiency: Expert-level fluency in Python and SQL, with the ability to write production-grade code for data pipelines and analytical models.

+ Domain Expertise: Deep technical understanding of cloud and hardware economics, including AWS/GCP cost models, on-premise compute lifecycles, and GPU/accelerator unit economics.

+ Modeling & Statistics: Proven experience designing attribution models and allocation frameworks. Ability to apply statistical methods, forecasting, and anomaly detection to predict capacity demand and optimize resource provisioning.

+ Observability: Hands-on experience with telemetry stacks (e.g., Prometheus, Grafana, Spark logs) to derive utilization insights.

+ Communication: Exceptional narrative skills. You can communicate complex quantitative findings to both engineering architects and finance leadership, influencing decisions through data-driven storytelling.

+ Strategic Agility: Comfort with ambiguity and a proven ability to define structure, taxonomy, and logic in new technical domains.

+ Bachelor’s, Master’s, or PhD in Computer Science, Economics, Engineering, Mathematics, or related field (or equivalent practical experience).

**Preferred Qualifications**

+ Advanced degree (PhD/Master’s) in Computer Science, Economics, Statistics, Operations Research, or a related quantitative field.

+ AI/ML Economics: Experience optimizing resource allocation for large-scale training and inference workloads (LLMs, Foundation Models).

+ Advanced Forecasting: Experience with recent advancements in forecasting, such as foundation models (e.g., TimesFM), Deep Learning approaches (RNNs, functional generative networks), or XGBoost/Ensemble models.

+ Judgmental Forecasting: Ability to incorporate qualitative business adjustments into model outputs, especially for unprecedented events.

+ Algorithm Design: Background in scheduling algorithms, bin-packing, or capacity allocation.

+ Open Source: Contributions to open-source observability, data engineering, or cloud efficiency frameworks.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088\_EEOC\_KnowYourRights6.12ScreenRdr.pdf) .