About the Role

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.

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

• Design & Build Financial Pipelines: Develop and maintain petabyte-scale data pipelines that ingest, normalize, and attribute usage telemetry (Compute, GPU, Storage, Network) from hybrid cloud environments.
• Implement Governance Logic: Write the code and rules engines for financial governance, including automated budget tracking, quota management systems, and anomaly detection alerts.
• Data Quality & Reliability: Own the health of financial datasets. Implement rigorous data quality checks (SLAs), lineage tracking, and auditing mechanisms to ensure reporting accuracy.
• Fin Ops Tooling: Build and expose APIs that deliver cost metrics to downstream engineering tools (e.g., CI/CD pipelines, chargeback dashboards, and resource tagging bots).
• System Optimization: Continuously tune and optimize data processing jobs (Spark/Flink) and storage layouts (Iceberg/Delta) to ensure the governance platform remains performant and cost-effective.
• Collaboration: Partner with Data Scientists and Platform Engineers to integrate economic models into production systems and ensure seamless data flow across the platform.

Minimum Qualifications

• 5+ years of experience in Data Engineering, Platform Engineering, or Backend Software Engineering.
• Big Data Proficiency: Strong proficiency in distributed data processing frameworks (e.g., Apache Spark, Flink, Trino/Presto) and modern table formats (Iceberg, Delta Lake).
• Coding Expertise: Strong, production-grade coding skills in Java, Scala, or Python, with a solid grasp of data structures, algorithms, and software design patterns.
• Infrastructure Knowledge: Familiarity with cloud infrastructure (AWS/GCP/Kubernetes) and the basics of cloud resource management (instances, storage classes).
• Data Modeling: Experience designing dimensional models and managing schema evolution for complex datasets.
• Problem Solving: Ability to debug complex distributed system issues and optimize code for performance and scalability.
• Education: Bachelor's, Master's, or PhD in Computer Science, Engineering, or related field.

Preferred Qualifications

• Cloud Cost Familiarity: Experience working with cloud billing data (AWS Cost Explorer, CUR files) or general cost management principles.
• Container Orchestration: Experience working with Kubernetes concepts (pods, namespaces, resource requests/limits).
• Streaming Data: Experience building real-time data pipelines using Kafka, Flink, or Spark Streaming.

Equal Opportunity

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.