Imagine what you could do here! The people here at Apple don’t just create products - they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. At Apple, inclusion is a shared responsibility, and we work together to foster a culture where everyone belongs and is inspired to do their best work. We’re a diverse collective of thinkers and doers, continuously reimagining our products and practices to help people do what they love in new ways. That innovation is inspired by a shared commitment to great work - and to each other. Because learning from the people here means we’re learning from the best. Retail Operations creates the tools and programs that empower our teams to provide, a one of a kind, only at Apple experience. We do this by obsessing over the employee and customer experience, and driving a global strategy that sets the bar. You will lead Store Analytics for Apple Retail and be a trusted partner to the Global Retail Leadership Team. Your team of data engineers, decision scientists and analysts will design, refine and create innovative analysis and data products and leverage large, complex data sources and market research to derive actionable insights and deliver dynamic and intuitive decision tools.

DESCRIPTION

Develop and implement a future-forward retail analytics strategy with a strong focus on AI/ML to support deep business insights and data-driven decision-making across the organization. Build strong team culture and the capabilities of individuals through effective employee development, involvement, communication and coaching. You take a hands-on approach to overseeing the development of predictive models and forecasting tools to anticipate demand and optimize store operations. Create and present compelling presentations to share your findings with the executive team. Regularly evaluate key performance indicators and benchmarks to measure store performance, customer behavior, and sales trends.

MINIMUM QUALIFICATIONS

• 15+ years of experience in Data Analytics/Business Intelligence or equivalent experience
• 10+ years of people leadership experience
• Some travel is required

PREFERRED QUALIFICATIONS

• Strong analytical and problem-solving skills, with the ability to derive actionable insights from complex datasets, and drive business strategy and decision making through partnership across the organization.
• Has a pulse on emerging retail analytics tools, technologies, and best practices, and provide recommendations for continuous improvement.
• Deep knowledge of Machine Learning, statistical analysis, forecasting methods, and data modeling techniques, and their application to the Retail industry.
• Passion for data visualization and information design, and experience with tools such as Tableau.
• Superb communication and presentation skills, with the ability to effectively convey complex analysis and data findings to both technical and non-technical stakeholders, including extensive experience presenting to leadership groups and executives.
• Thrives in an ambiguous, fast-paced environment operating at both strategic and tactical levels with a strong attention to detail.
• Ability to work collaboratively in a cross-functional environment and effectively manage multiple priorities.
• Proven leadership in establishing and implementing a vision, goals and strategic and tactical execution plans.
• Proven people manager who builds capabilities of individuals and teams through effective employee development, involvement, communication and coaching
• Actively contribute to an inclusive environment through inclusive hiring, encouraging all voices to be heard and valuing those voices, and serving as an example of leading a culture of belonging for others across Apple.
• Bachelors degree and/or Master’s degree in relevant field (MBA a plus) or equivalent experience preferred

Please refer the Job description for details