Senior Data Science Manager - Strategic Data Solutions

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. Apple's Strategic Data Solutions team protects customers and the business from fraud and abuse across retail, online payments, and devices, at a scale few organizations ever encounter. We are looking for a data science manager who brings the technical depth of a senior practitioner and the judgment of a people leader. You will lead a team of highly technical data scientists who pair analytics engineering with deep statistical expertise, building the data foundations that power fraud decisioning and the rigorous analysis that shows what is working. You will personally own the most consequential analyses, pipelines, and measurement frameworks, working directly with machine learning engineering and business leaders to define how success is measured, uncover what is driving shifts in adversarial behavior, and turn those insights into automated decisions that protect millions of customers.

This role is designed for someone who wants both the scope to shape a team and the opportunity to stay close to the hardest technical problems. You will spend a meaningful share of your time on hands on analysis, pipeline development, and methodology review, and your own work will set the analytical standard for the team. We are looking for someone who actively uses AI to accelerate their own work and who will help the team adopt new tools and practices as the field evolves. Hire, grow, and lead a team of data scientists who combine analytics engineering with deep statistical expertise, raising analytical quality through methodology review and your own technical contributions Lead through influence, balancing hands on technical mentorship with the strategic judgment to deliver scalable, high impact outcomes Build and maintain production data pipelines and reusable data models that power fraud analytics and decision automation, providing architecture guidance while ensuring data integrity, accessibility, and consistency across systems Design and implement robust monitoring, alerting, and health check systems that track data quality and model performance in production, surfacing issues before they affect fraud decisions Define success metrics and drive experimentation, causal inference, and statistical analysis, including designing rigorous approaches where conventional A/B testing is impractical Personally lead the most ambiguous and high stakes investigations, such as emerging attack patterns, enforcement anomalies, and unexpected shifts in model performance, and translate them into well scoped analytical questions with clear decision criteria Partner with machine learning engineering leaders to power decision automation, and with program and operations leaders to align analytical priorities, using compelling data storytelling, visualizations, and narratives to turn findings into decisionsBachelor's degree in Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field 6+ years of hands on experience in data science, analytics engineering, or similar technical analytics roles, including 1+ years leading data scientists as a people manager or technical lead Advanced proficiency in SQL and Python with experience writing production quality code, along with working knowledge of data warehouses, data lakes, and distributed processing of large datasets Strong background in statistical analysis, experimentation, and causal inference, including designing approaches when a clean randomized test is not available Experience building and maintaining production data pipelines and analytics systems, with a demonstrated record of measurable business impactExperience in fraud, risk, trust and safety, payments, or other adversarial domains Experience building and maintaining data transformation pipelines in dbt, including modular model design, testing, documentation, and version controlled development practices Familiarity with modern data platforms such as Snowflake, Spark, and Airflow, and fluency in software development lifecycle practices, including CI/CD, version control, and automated testing Track record of building analytics frameworks or tools adopted beyond your immediate team Expertise in data storytelling, including stakeholder ready visualizations and narratives that influence senior leadership decisions, along with a commitment to building inclusive teams that value diverse perspectives