Lead Data Scientist

Disney Disney · Media · San Francisco, CA +3

Lead Data Scientist responsible for driving innovation in personalization, experimentation, and large-scale data insight generation across ESPN’s streaming products. This role blends deep statistical expertise, strong technical execution, and exceptional communication skills to influence product strategy and guide a growing team of data scientists. The role will lead the development of advanced experimentation frameworks, actionable dashboards, personalization insights, and scalable data science solutions, while championing automation and AI-assisted workflows.

What you'd actually do

  1. Provide technical leadership for the data science team, guiding junior and mid‑level data scientists through best practices in experimentation, modeling, analytics, and data storytelling.
  2. Identify, analyze, and deliver high‑impact insights for ESPN personalization—including user behavior trends, recommendation system performance, and content engagement patterns—to influence product decisions.
  3. Design and analyze large‑scale A/B and multivariate experiments using rigorous statistical methods, power analysis, variance reduction techniques, and causal inference frameworks.
  4. Lead the design of automated dashboards, visualizations, and decision‑support tools, while partnering with Data Engineering to build durable, production‑ready data pipelines and science workflows.
  5. Own end‑to‑end delivery of complex data science initiatives by collaborating with Product, Engineering, Content Programming, and Executive stakeholders, translating technical findings into actionable recommendations.

Skills

Required

  • Python
  • SQL
  • modern ML/statistics libraries
  • large-scale datasets
  • distributed computing environments
  • Spark
  • Databricks
  • Snowflake
  • data engineering best practices
  • dashboarding tools
  • Tableau
  • Looker
  • Mode
  • automation
  • AI-driven tools
  • communication skills
  • translating technical analyses into compelling stories

Nice to have

  • personalization algorithms
  • recommender systems
  • ranking models
  • streaming or media tech ecosystems
  • PyTorch
  • TensorFlow

What the JD emphasized

  • Proven track record of delivering high-quality experimentation analyses and actionable insights that influenced product strategy.
  • Deep expertise in statistical testing, causal inference, and experiment design (A/B testing, power analysis, sequential testing, etc.).
  • Experience applying automation and AI-driven tools to streamline workflows, improve standardization, and optimize productivity.

Other signals

  • personalization
  • experimentation
  • large-scale data insight generation
  • recommendation system performance
  • content engagement patterns
  • A/B and multivariate experiments
  • automated dashboards
  • visualizations
  • decision-support tools
  • scalable data science solutions
  • AI-assisted workflows