Data Scientist II

Overview

We are looking for an exceptional Data Scientist with 3+ years of experience to help Uber's internal and external applications, products and services to grow adoption, improve CSAT and reduce cost. You will leverage analytics, engineering, and machine learning, and data science to empower data-driven decision making in the full lifecycle of product development. You will be responsible for building this program from the ground up: partner with stakeholders, understand all aspects of the products, interact with cross-functional business and technical teams, predict risks, improve strategic decisions, and lead experimentation/growth projects.

What The Candidate Will Need / Bonus Points

---- What the Candidate Will Do ----

• Apply your expertise in quantitative analysis, data mining, and statistical modeling to deliver impactful, objective, and actionable data insights that enable informed business and product decisions
• Define, measure, and analyze business metrics and product KPIs
• Create tableau dashboards and the underlying data aggregations in AWS.
• Conduct exploratory analysis to inform product strategy and evaluate recently released or existing products or features
• Partner with multiple team members to make data-driven decisions across the organization by leveraging descriptive and predictive analytics
• Work from the inception of a specified business problem identified by data to delivery of a solution, identify opportunities to fix or improve processes that make Uber better in the long term
• Put together seemingly disparate pieces of data, build out the data pipeline and create interactive dashboards, provide comprehensive analytics support to partner teams, primarily through development of self-serve tools (such as Tableau and Looker)
• Write queries, contribute to larger-scale products/projects and perform ad hoc analyses as needed, apply a diverse set of techniques including statistics, quantitative reasoning to help grow the business
• Comfortable with working in fast-paced environments to find answers to critical questions with data and be decisive in an ambiguous situation
• Determine and monitor essential metrics for product projects
• Own, drive, and solve complex, cross-functional problems that extend beyond the traditional boundaries of product domains, analytics, and data science
• Communicate analysis and decisions to high-level stakeholders and executives in verbal, visual, and written format
  
  

• 3+ years in quantitative/analytic role, preferably within tech
• 1+ years experience with BI Tools such as Tableau, MicroStrategy, Power BI or Looker
• Experience in applying both data-backed heuristics and machine-learning techniques to solve practical product problems such as customer lifetime value models, funnel optimization.
• Expertise using data modeling skills to identify key product trends and new product opportunities
• Ability to design implement, and track core metrics to analyze the performance of our products
• SQL expert and a good understanding of databases, data relationships and data integrity
• Demonstrated ability to think strategically about business, product, and technical challenges
• Strong communication skills and customer empathy, capable of pushing for the best solution with tact
• Strong problem solving skills and ability to translate ambiguous, unstructured problems into actionable data-driven analyses
• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related technical field
• Excellent planning and project management skills
  
  

• Advanced degree in applied mathematics, statistics, actuarial science, economics or related field
• Experience in a product-focused role at a social media and/or mobile technology company
• Experience handling structured and unstructured data from internal and third-party sources
• Experience with Python, Javascript, and R are plus
• Strong communication and interpersonal skills
• Exposure to Web Analytics such as Google Analytics, Adobe Analytics/Omniture, Heap Analytics
• Experience with causal inference techniques, experimental design and/or A/B testing
• Leveraging and integrating large language models (LLMs) to enhance analytics, automate workflows, and improve user interaction and decision support."