AIML Data Operations - Director, Business and Capacity Planning, Data Analytics

Apple•Cupertino, CA

About The Position

The AIML Data Operations group engages with teams across Apple’s ecosystem with the goal of delivering high-quality annotated data in support of unreleased products and ground breaking AI technology. The Data Operations Planning and Analytics team provides leadership in forecasting, capacity planning and optimization, annual planning, data products, metrics reporting, and modeling & experimentation. The AIML Data Operations team is dedicated to technology, with a primary focus on enhancing the customer experience. The Director of Business and Capacity Planning and Data Analytics, is responsible for shaping the organization's business and capacity plans and seeing the organization more clearly through data. Working closely with executive leadership, cross functional EPM’s and Engineering DRI’s, and business leaders, this role builds a high-performance culture. The ideal candidate for this role is an experienced leader who loves working with people across technical and non-technical domains to solve problems and deliver results. You will work with teams in finance, operations, engineering, and program management to forecast demand, develop capacity plans, and resulting HC plans and annual plans and partner in the execution of annotations projects that support teams across Apple’s ecosystem who are working on cutting-edge AI and machine learning technology.

Requirements

Bachelors degree in Computer Science, Statistics, Mathematics, Economics or related field.
15+ years of experience in Forecasting, Planning, Data Analyst, or Data Science roles
Proficiency with using large scale data analytics in service of solving complex business problems
Understanding of Python or R, SQL, tools such as scikit-learn, and forecasting libraries.
Extensive experience building production-ready models for forecasting applications.
Ability to condense complex concepts and analysis into clear and concise takeaways that drive action.
Superb communication, story telling, and presentation skills with meticulous attention to detail.
Excellent in teamwork and relationship building.
Self-directed, intellectually curious, and proactive.
You thrive in an ambiguous and fast-paced environment.

Nice To Haves

Master's degree or relevant professional certifications in a related field.

Responsibilities

Develop annual capacity plans across engineering organizations, teams, skills, and geographies.
Translate business strategy and product priorities into engineering capacity requirements.
Balance headcount, contractors, vendors, automation, AI-assisted development, and platform investments.
Identify capacity constraints, skill gaps, and organizational bottlenecks early.
Drive the annual planning process from strategic priorities through resource allocation and execution targets.
Establish planning assumptions, scenarios, and trade-offs.
Partner with Engineering, Product, Finance, HR, and executive leadership to align plans.
Build mechanisms for re-planning as business priorities and technology directions change.
Lead a team of highly capable data scientists, business analysts and EPM’s.
Understand data and prepare projects for execution, partnering with engineering teams to improve project execution.
Engage with engineering, data science, and quality assurance teams across Apple's ecosystem to optimize the distribution of annotation workloads, ensuring the successful timely delivery of high-quality labeled data.
Convert internal customers' project needs into quantifiable demand requirements to determine optimal workforce sizing, selection, and scheduling strategies necessary to deliver projects successfully.
Continually assess existing workforce capability against internal customer data annotation requirements, making real-time decisions to expand workforce availability and ensure on-time, high-quality delivery.
Research and evaluate emerging tools and techniques in the forecasting space to advance and improve the suite of capacity planning models tools used for efficient production brokering.
Partner with internal and external stakeholders to identify automation opportunities, define technical requirements, develop AI/ML models, and facilitate stakeholder evaluation and testing cycles.
Communicate performance trends to leaders on an ongoing basis, highlighting and spearheading opportunities to drive further efficiency across the data operations organization.