Lead Data Analyst, UX Analytics

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Summary

Disney Entertainment and ESPN Product & Technology is seeking a Lead Data Analyst for the User Experience Analytics team. The team focuses on understanding how users navigate and engage with Disney's streaming products and whether the feature changes we make deliver the intended results.

The work is primarily experimentation and analysis. When Disney+ rolls out a new navigation bar, introduces autoplay video in the browse experience, or launches short-form video on mobile, this team defines the metrics, designs the measurement approach, runs the experiments, analyzes the results, and delivers a clear recommendation. Findings inform decisions at every level of the product org: some analyses redirect a feature in development, others validate or quantify the risk of a direction already in motion. Many of our projects are high-priority and high-visibility, with outputs regularly presented to PM stakeholders as well as VP and SVP audiences.

The Lead Analyst owns the most complex work on the team end-to-end: the framing, the approach, the read, and the recommendation.

What You'll Do

• Own experiment analysis end-to-end. Lead A/B tests on navigation, discovery, and feature surfaces from metric selection and test design through power analysis and final recommendation.
• Develop and own metrics. Define what success looks like for a product surface before an experiment runs. The frameworks you build are what the team and its stakeholders align on.
• Drive behavioral analysis. Answer how users find, engage with, and return to content.
• Communicate across levels. Present findings to PMs and prepare readouts for VP and SVP audiences on high-priority projects.
• Build and maintain dashboards. Provide continuous monitoring for key product surfaces.

What to Bring

• Bachelor’s Degree in a related field
• 7+ years in data analytics, with a track record of work that influenced a decision, not just informed a deck
• Deep experimentation experience: test design, power analysis, metric selection, and reading results correctly, including local vs. global metrics
• Strong metric development instincts: defining what to measure, why, and what the failure modes are before data collection starts
• Advanced SQL for behavioral data at scale: pathing, funnels, cohort analysis
• Proficiency in Python for scripting, text manipulation, and analytical tasks that SQL cannot handle. SQL is the primary tool for analysis
• Building visual dashboards using Looker (LookML); familiarity with Tableau
• Exceptional communication and presentation skills, with experience delivering insights to stakeholders at all levels

Nice-to-Haves

• Streaming or subscription product background
• Causal inference methods beyond standard A/B (diff-in-diff, synthetic control)
• Advanced degree in a quantitative field

#TECHATDISNEY

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