The Retail Online Analytics team is responsible for providing insights, analytical solutions, and intelligent systems to Commercial Management, Commercial Marketing, Digital Merchandising, Online Product Management, Retail Contact Center, as well as Finance and other partner teams.
The Manager of Online Data & Analytics leads a multidisciplinary center of excellence within Apple Retail Online, spanning Advanced Analytics, Machine Learning Engineering, Data Engineering, and AI-powered Decision Support. Reporting to the Global head of Retail Online Analytics, this role drives the strategy, architecture, and delivery of data-driven insights, scalable ML systems, and intelligent applications that directly improve customer experience and business outcomes for the Apple Store Online organization.
Description
We are seeking an experienced senior professional to lead a multidisciplinary team and build a center of excellence for online analytics, data engineering, machine learning, and AI-driven decision support. The team encompasses advanced analytics, ML engineering, and data architecture & engineering functions. This team is tasked with the development of analytics and ML solutions, scalable data infrastructure with modern CI/CD practices, AI-powered decision support applications, and reusable analytics libraries for performance measurement and activation of insights to improve customer experience and make a direct impact on business outcomes.
This individual is highly proficient with advanced time series analysis, e-commerce analytics, machine learning systems, data architecture & engineering, emerging AI technologies, and ML Ops. They excel at turning data discoveries into analytical insights and intelligent applications that drive business and customer outcomes. The role requires a mix of high-level leadership, technical vision across the data and ML stack, and deep experience advising the activities of a highly applied and results-focused team.
You are skilled analytically and have a deep understanding of the businesses you support. You will be a thought partner to your business partners, understand their goals and objectives, and leverage your analytical skills, ML capabilities, and AI tools to surface meaningful insights and build systems that scale decision-making.
We seek someone with a strong business demeanor and outstanding technical skills, who possesses the ability to condense sophisticated analysis, ML model outputs, and AI system capabilities into clear and concise takeaways for business leaders.
Minimum Qualifications
Advanced degree in a quantitative field such as Statistics, Computer Science, Applied Social Sciences, Engineering, or Mathematics.
10+ years of experience in Marketing Science, Advanced Analytics, or Data Science.
5+ years of experience with big data computing, machine learning techniques, and data engineering at scale.
5+ years of people management experience leading cross-functional technical teams.
Experience with ML model productionalization and modern data engineering practices including CI/CD, orchestration, and data quality monitoring.
Proficiency with big data and ML frameworks such as Spark, Hadoop, Scikit-learn, XGBoost, or PyTorch.
Expert-level knowledge of applied regression techniques including Linear, Logistic, Mixed Models, Distributed Lags, Time Series, GLM, and Simultaneous Equations.
Proven track record of leading virtual/distributed teams and influencing without direct authority.
Strong verbal and written communication skills with the ability to present to senior business leaders.
Experience supporting eCommerce and Digital Marketing partners.
Preferred Qualifications
Familiarity with AI/ML application development including agentic systems, NLP, or Generative AI technologies.
Experience deploying LLM-based agents, RAG pipelines, or other AI-powered decision support systems in a production environment.
Deep expertise in marketing mix modeling and media attribution across digital and offline channels.
Experience with advanced analytic solutions for customer insights and media planning (attribution, forecasting, market basket analysis, purchase probability).
Strong command of data engineering principles including ETL/ELT design patterns, data quality frameworks, infrastructure-as-code, and automated testing.
Demonstrated ability to measure interaction effects of marketing channels across product launches, promotions, and business-as-usual periods.
Experience with supervised, unsupervised, and reinforcement learning techniques applied to real business problems.
Consistent track record of initiating cross-organizational projects and building influence with executive stakeholders.
Outstanding problem-solving skills with strong attention to detail and creative resourcefulness.
