Summary

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.

As a Data Scientist, you will provide data driven insights to fight fraud. You will develop models, evaluate product launches, develop automated solutions to deliver timely alerts, find opportunities for future development by applying the bleeding edge scientific methods. You will partner with engineers, product, program and business leaders alongside other teams to bring better experiences to drive meaningful customer impact.

Description

Research and develop evaluation methods to address fraud. Solve difficult, non-routine analysis problems by applying statistical, machine learning and advanced analytical methods as needed.

• Design and execute observational and experimental studies of causal inference
• Work with large, complex data sets.
• Develop automated solutions to deliver insights and alerts
• Drive feature evaluation and product roadmap with insights
  
  

Responsibilities

• Develop scalable data solutions to be used to drive analyses, reports, and insights
• Influence upstream data model design, drive KPI definitions and develop customized data solutions
• Measure impact of features and initiatives and help improve customer experience
• Collaborate and influence cross functional partners to help deliver product objectives on time
• Communicate results, insights and expectations to partners and senior leaders, bridge any gaps between technical and non-technical audiences. Be adept at messaging domain and technical content at a level appropriate for the audience.
• Work independently in sophisticated and highly visible projects, identify risks and develop frameworks, regularly connect with collaborators and leadership teams.
  
  

Minimum Qualifications

• Masters in Statistics, Mathematics, Data Science, Machine Learning, Physics, Engineering, Computer Science or equivalent
• Proficiency in data querying using SQL, Spark, or equivalent technologies
• Proficiency in scripting languages for data processing and analysis (ex: Python, R, or Scala)
• Excellent communication and multi-functional collaboration skills, ability to convey complex concepts to diverse audiences
• At least 3 years experience working as a Data Scientist
  
  

Preferred Qualifications

• PhD in Statistics, Mathematics, Data Science, Machine Learning, Physics, Engineering, Computer Science or equivalent
• Experience applying LLMs to solve technical problems such as data analysis, data automation, synthetic data generation with proven ability to optimize model performance for accuracy and efficiency
• Excellent product intuition, keen eye for design and customer pain point awareness
• Experience developing anomaly detection and causal inference models
  
  

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.

Apple accepts applications to this posting on an ongoing basis.