Senior Data Science Manager - Worldwide Product Marketing

As a Senior Data Science Manager, you will lead and grow a high-performing team of data scientists driving analytics, experimentation, modeling, and data-driven insights that shape business strategy and product experiences. Operating at both the strategic and execution levels, you will set the team's analytical vision and roadmap while staying close enough to the work to guide modeling, machine learning, and production readiness. As a trusted advisor to leadership, you will bring clarity to complex problems and influence decisions with evidence-based recommendations.

Description

• Lead data science initiatives end-to-end—from scoping and data prep to modeling, visualization, and delivery
• Provide technical direction and review analytical approaches, ML models, and dashboards
• Set standards for code and data quality, reproducibility, and production readiness, partnering with Engineering and ML teams on scalable solutions
• Champion the responsible use of LLMs and AI-assisted tooling to accelerate insights, visualization, and modeling
• Set the analytical vision and multi-quarter roadmap, aligned to business priorities and Apple's broader goals
• Prioritize competing initiatives, balancing quick wins against long-term platform and capability investments
• Anticipate where the discipline is heading—including AI/LLM and agentic workflows—and shape the team's capabilities accordingly
• Translate business questions into analytical frameworks, and analytical results into actionable decisions
• Serve as the primary thought partner for Finance, Marketing, Engineering, Product, and cross-functional stakeholders

Minimum Qualifications

10+ years of experience in data science, analytics, or applied machine learning
2+ years leading, mentoring, and scaling data-focused teams
Bachelor's degree in Computer Science, Statistics, Applied Math, Engineering, or a related field

Preferred Qualifications

Strong foundation in statistics, experimentation design, causal inference, and ML methodologies
Proficiency in SQL and Python
Experience with large, complex datasets, data pipelines, and production-level analytics systems
Proven ability to drive measurable business impact through data and automation
Exceptional communication skills, able to influence technical and non-technical stakeholders
Ability to operate effectively in ambiguous, complex environments and set clear direction