Sr Data Scientist, Tech

Uber Uber · Consumer · New York, NY · Data Science

This role focuses on building prototype software solutions to automate business processes and drive operational efficiency using data and AI technologies. The candidate will leverage data to understand business problems, identify automation opportunities, conduct architecture and code reviews, and manage teams. The role involves developing end-to-end solutions for funnel optimization, user engagement, and user experience, and identifying use cases for machine learning and AI capabilities.

What you'd actually do

  1. Build prototype software solutions to automate business processes to drive operational efficiency.
  2. Leverage data to understand business problems and opportunities for automation, enabling teams to action levers that drive incremental business value without increasing manual burden.
  3. Conduct architecture and code reviews with Engineering teams to enable prototype solutions through internal and external APIs, finding opportunities to apply new AI technologies and tools.
  4. Monitor and track performance of automated activities to ensure business continuity and re-assess relevance periodically, and move beyond the prototype stage where relevant in partnership with Engineering and Product teams.
  5. Manage an in-person team that carries out similar duties as above, and oversee a remote team of analysts that create dashboards and reports to communicate results and monitor key metrics, through analysis and building data pipelines.

Skills

Required

  • R or Python
  • SQL
  • Designing and overseeing data products, analytics, and automation solutions
  • Leading technical teams in automation, analytics, or data science
  • Developing end-to-end prototype solutions to automate business processes and improve efficiency
  • Identifying opportunities for business and operation optimization and efficiency
  • Machine learning and AI capabilities and identifying use cases to drive automation
  • Deploying code and developing best practices for automation capabilities

What the JD emphasized

  • automate business processes
  • operational efficiency
  • apply new AI technologies and tools
  • develop end-to-end solutions
  • Machine learning and AI capabilities

Other signals

  • automate business processes
  • operational efficiency
  • apply new AI technologies and tools
  • develop end-to-end solutions