Manager, Product Management Data Scientist

Apple Apple · Big Tech · Cupertino, CA +1 · Marketing

Manager, Product Management Data Scientist at Apple, leading a team to generate AI-powered customer insights for product decisions. The role involves defining analytical frameworks, identifying product opportunities, and translating data into recommendations using modern AI and LLM tooling. Responsibilities include leading end-to-end analytical initiatives, collaborating with cross-functional teams, and leveraging LLMs and agentic AI workflows to accelerate insight generation and modeling. Requires strong Python, data pipeline, and database experience, with a focus on AI-assisted analysis, foundation models, LLMs, and agentic systems.

What you'd actually do

  1. Define and lead rigorous, innovative analyses that deepen our understanding of customers and products
  2. Identify new and creative ways to bring data to critical product decisions
  3. Present findings to technical and non-technical audiences with clarity, precision, and confidence
  4. Leverage LLMs and agentic AI workflows to streamline analysis, automate repetitive tasks, and accelerate insight generation, visualization, and modeling
  5. Help shape analytical standards and best practices on the team, including how we adopt and apply emerging AI tooling

Skills

Required

  • Python (pandas, NumPy, SciPy)
  • Data pipeline tools (PySpark, Spark)
  • Databases (Postgres, sqlite)
  • AI-assisted analysis workflows
  • Foundation models and LLMs
  • Agentic systems for technical problem solving
  • Driving technical initiatives in ambiguous, cross-functional environments
  • Communication skills
  • Mentoring analysts

Nice to have

  • People management experience
  • Leading teams collaborating with engineering, design, and marketing
  • Translating business questions into technical and data analysis
  • Fostering collaborative team environment
  • Uncovering insights through data exploration and analysis
  • Leading through influence, debate
  • Business acumen
  • Creative solutions and alternative paths for problem solving
  • Designing and deploying LLM-based and agentic workflows
  • Managing end-to-end analytics projects
  • Working with incomplete or ambiguous data

What the JD emphasized

  • 7+ years of experience and a graduate degree in Computer Science, Statistics, Data Mining, Machine Learning, Analytics, Econometrics, Mathematics, or similar quantitative field or bachelor's with 10+ years of relevant experience
  • Strong proficiency in Python (i.e. pandas, NumPy, SciPy), data pipeline tools (PySpark, Spark), and experience with building and manipulating databases (Postgres, sqlite)
  • Experience with developing AI-assisted analysis workflows, working with foundation models and LLMs, or using agentic systems for technical problem solving (i.e. creating AI tools and logic that directly influences R&D product decisions)
  • Demonstrated experience driving technical initiatives in ambiguous, cross-functional environments, with a solutions-oriented mindset, rallying teams to drive towards outcomes despite ambiguity or resistance
  • Exceptional communication skills, with the ability to simplify complex topics and engage stakeholders at all levels
  • Mentored and supported other analysts on a team, providing guidance on analytical approaches, tooling, and best practices
  • 2+ years of prior people management experience
  • Proven record leading teams that effectively collaborate with engineering, design, and marketing peers
  • Experience designing and deploying LLM-based and agentic workflows to improve analytical productivity
  • Experience managing end-to-end analytics projects with multiple competing priorities

Other signals

  • Leverage LLMs and agentic AI workflows to streamline analysis, automate repetitive tasks, and accelerate insight generation, visualization, and modeling
  • Experience with developing AI-assisted analysis workflows, working with foundation models and LLMs, or using agentic systems for technical problem solving
  • Experience designing and deploying LLM-based and agentic workflows to improve analytical productivity