Staff Data Scientist, Platform Economics, Apple Data Platform

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.","responsibilities":"As the lead architect for Platform Economics, you will move beyond simple reporting to build a predictive system that optimizes the "supply and demand" of Apple’s infrastructure. You will:

Design Economic Architectures: Architect scalable attribution models that accurately map complex workloads (Compute, GPU, Storage, Network) to product value, ensuring fair and transparent accounting across the organization.

Engineer the Data Foundation: Lead the development of data pipelines and telemetry standards that collect, normalize, and enrich usage data from across Apple’s hybrid cloud environment.

Drive Algorithmic Efficiency: Analyze utilization patterns to detect structural inefficiencies-identifying idle capacity, unoptimized query patterns, or cold data-and algorithmically surface these opportunities to engineering teams.

Define the North Star: Establish the "Unit Economics" and efficiency KPIs that will serve as the primary metrics for engineering performance and resource density.

Forecast & Plan: Collaborate with Capacity Planning to build forecasting models that predict future infrastructure demand based on historical growth, architectural shifts, and product roadmaps.

Automate Governance: Partner with service engineers to design policy-driven automation (e.g., auto-scaling logic, quota management) based on the economic models you define.

Cultivate Data Culture: Mentor engineers and stakeholders on the financial impact of architectural decisions, helping them navigate the trade-offs between performance, speed, and cost.

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.

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).

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 .