Data Analytics Specialist IV

About the Role

As a senior member of the Data & Analytics team within Community Operations, you will act as a strategic partner. Your role involves transforming complex datasets into a competitive advantage for Uber's global support network by building statistical models and automated tools. This position requires a mindset that thrives on complexity, navigates technical debt and shifting priorities, and balances deep-dive data mining with stakeholder management.

Key Responsibilities

• Conceptualize and build high-impact, automated reports and dashboards to serve as the single source of truth for global operational efficiency.
• Perform advanced data mining and statistical modeling to identify trends and operational weaknesses, addressing inefficiencies proactively.
• Lead business deep dives to generate actionable insights, translating complex technical findings into clear narratives for leadership.
• Build and maintain scalable statistical tools to enable data-backed, real-time decision-making for operational teams.
• Measure the real-world impact of new processes and policy changes to inform scaling or pivoting of support strategies.
• Mentor and train team members on technical best practices in SQL, Python/R, and data modeling.
• Navigate and align conflicting stakeholder priorities, managing projects end-to-end to ensure timely delivery of analytical solutions.

Requirements & Tech Stack

• Strong proficiency in SQL and experience with web-based query tools.
• Hands-on experience with Python for data manipulation.
• Experience with data visualization tools (e.g., Tableau or Looker).
• Proven stakeholder management skills.
• Basic project management experience.
• High school diploma or equivalent experience.

Preferred Qualifications

• Experience with Python for building predictive models or complex data pipelines.
• Experience with Google Scripting to automate internal workflows.
• Demonstrated ability to navigate high-ambiguity environments and lead through influence.
• Systems-thinking approach with a track record of building long-lasting artifacts that reduce technical debt.
• Experience in working with AI tools in relation to data processes.