RCC Data Scientist

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.

Here on the Apple Store Online team, we are responsible for Apple’s largest store. Our main goal is to deliver a magical, personal digital experience where customers can shop, buy and learn everything Apple, wherever they are. Each customer should feel like they are our only customer and our job is to set the bar for the experience they receive. To run such an extraordinary store, it takes extraordinary people, and we are looking for someone to help us do extraordinary things.

We are seeking a highly motivated Senior Data Scientist to shape our innovation strategy through advanced behavioral segmentation, pilot measurement, text analytics, and AI tools integration. In this role, you will be part of Apple Store Online, with a particular focus on Retail Customer Care (RCC) performance and decision support. Using data and quantitative techniques, the decision support team acts as a consultant, providing insights and recommendations to inform the organization’s innovation strategy and to drive operational performance excellence across Apple and vendor contact center sites.

Description

In this role, you will define how we measure the performance of our end-to-end customer interaction systems and build analytical frameworks to compare outcomes across different interaction models. Working with structured operational data and unstructured conversation data at scale, you will apply NLP, experimentation, and statistical modeling to drive decisions that directly impact revenue, cost, and the customer experience. You will be a part of the Business Operations Analytics team within the Retail Contact Center, partnering closely with a wide range of cross-functional stakeholders to measure and optimize performance across our contact center operations.

Minimum Qualifications

Bachelor’s degree in computer science, mathematics, finance, statistics, econometrics, or other quantitative field, and 7+ years of professional experience in data science, machine learning, or advanced analytics within business analytics, e-commerce, and customer analytics. Hands-on experience with text analytics, NLP, and sentiment analysis — including working with conversation or transcript data at scale. Experience defining and establishing performance metrics and KPIs — particularly for new programs, systems, or technologies where measurement frameworks don't yet exist. Experience designing and analyzing A/B tests and/or experience with quasi-experimental methods (e.g. causal inference, diff-in-diff, propensity matching, synthetic controls) to measure the impacts of business interventions. Strong programming skills in Python and SQL for large-scale data manipulation and analysis. Proficiency in data visualization tools (Tableau preferred). Strong communication and storytelling skills — the ability to translate complex analytical findings into clear recommendations for both technical and non-technical audiences, including senior leadership.

Preferred Qualifications

Master's degree in a quantitative field preferred. Familiarity with modern cloud data platforms, particularly Snowflake and AWS. Deep domain experience in contact center operations, customer experience analytics, or customer interaction measurement. Hands-on experience leveraging LLMs and Generative AI to enhance analytical productivity and generate new insights. Proven track record of stakeholder management and large-scale cross-functional project work. Demonstrates Apple’s values. Contributes to an inclusive environment through respecting each other’s differences and having curiosity to learn.