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
Basic Qualifications ----
- 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
- Bachelors degree in Computer Science, Mathematics, Statistics, or a related technical field
- Excellent planning and project management skills
Preferred Qualifications ----
- 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."
