Job Details

Weekly Hours: 40

Role Number: 200635136-0836

Summary

We are seeking an exceptional Senior Data Scientist to join our Experimentation Data Science team. In this role, you will lead the development of cutting-edge statistical and causal inference methods, design and analyze large-scale experiments on Apple Media products, and partner closely with cross-functional teams to advance our strategic decision-making. You will bring deep research expertise, strong technical acumen, and the ability to translate complex insights into practical business recommendations.

This is a high-impact role for someone who thrives at the intersection of research, experimentation, and real-world application—and who is passionate about shaping data science excellence at scale.

Description

Conduct research and develop novel statistical and causal inference methodologies applicable to experimentation at scale.
Publish, present, and champion new techniques that push the boundaries of real-world data science.
Design, implement, and analyze A/B tests and quasi-experiments across a variety of product, platform, and business domains.
Apply advanced causal inference methods (e.g., matching, synthetic controls, IV, DiD, uplift modeling) to generate robust, reliable insights.
Work fluently with large-scale data systems, including HDFS, Spark, Scala, and distributed computing frameworks.
Develop and leverage modern data tooling to support fast, scalable experimentation.
Build high-quality data products using Python, R, and related open-source tools.
Clean, synthesize, and analyze complex datasets with rigor and efficiency.
Translate ambiguous business questions into well-structured analytical approaches and experimental designs.
Deliver clear, actionable recommendations that inform strategy and accelerate impact.
Present complex analytical findings to technical and non-technical audiences with clarity, precision, and confidence.
Develop compelling presentations and visualizations that communicate insights effectively and drive decision-making.
Collaborate with product managers, engineers, design teams, and other data scientists to scale experimentation and causal inference best practices across the organization.
Mentor others, contribute to team standards, and model excellence in scientific rigor and collaboration.
Demonstrate a strong sense of ownership, accountability, and a passion for elevating experimentation science.
Continuously learn, explore new methods, and adapt to evolving technologies and business needs.

Minimum Qualifications

• PhD in Statistics, Computer Science, Economics, Mathematics, or a related quantitative discipline, with a strong publication record in top-tier journals or conferences.

• Extensive experience with advanced statistical methodology, experimentation frameworks, and causal inference techniques.

• Hands-on expertise with big data ecosystems, including HDFS, Spark, and Scala.

• Proficiency in Python and/or R, with strong software engineering and data manipulation skills.

• Exceptional communication skills, with the ability to simplify complex topics and engage with stakeholders at all levels.

• Proven ability to translate business needs into scientific solutions, balancing rigor with practicality.

• Strong presentation and data visualization skills (e.g., ggplot, matplotlib, Plotly, Shiny, dashboards, storytelling tools).

Preferred Qualifications

• A collaborative mindset, excellent organization, and a passion for scaling processes and sharing knowledge.

• A growth-oriented, curious, and adaptive approach to your work and the evolving data science landscape.

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 (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .