Senior Data Scientist

Summary
Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish!

Apple's Sales organization generates the revenue needed to fuel our ongoing development of products and services. This, in turn, enriches the lives of hundreds of millions of people around the world. We are, in many ways, the face of Apple to our largest customers.

Apple's US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating solutions that have a direct and measurable impact on Apple Sales and its customers.

As a US DI Data Scientist, you will employ predictive modeling, data visualization, and statistical analysis techniques to build end-to-end solutions for internal stakeholders, leveraging sales performance data, market data, programs, external data, etc.

This role will operate in both capacities, to augment existing data solutions, as well as innovate and trailblazing new data science projects across our RTMs, crafting analytical experiences that transform data into insights and catalyze better decision-making.

Analytics is a team sport, and in your role, you will be key in leading and influencing teams on the translation of business problems and questions into data science solutions.



Description
In this role, you will:

- Act as a data translator, bridging the gap in expertise between technical teams, made up of data analysts, data scientists, data engineers, software developers, and business stakeholders. - Successfully connecting analytics and business, with the ability to speak the language of both.
- Influence upstream data model design, drive KPIs definitions, and develop your e2e data solutions as needed.
- Establish a comprehensive roadmap to communicate and manage our commitments and stakeholder expectations while enabling org-wide visibility on progress.
- Build and support dashboards and self-service tools (using several platforms) to analyze and present internal and external data.
- Leverage available ML/AI models and frameworks across the company.

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