The Core Analytics & Science Team (CAS) is Ubers primary science organisation, covering both our main lines of business as well as the underlying platform technologies on which those businesses are built. We are a key part of Ubers cross-functional product development teams, helping to drive every stage of product development through data analytic, statistical, and algorithmic expertise. CAS owns the experience and algorithms powering Ubers global Mobility and Delivery products. We optimise and personalise the rider experience, target incentives and introduce customizations for routing and matching for products and use cases that go beyond the core Uber capabilities. What Youll Do: Refine ambiguous questions and generate new hypotheses about the product through a deep understanding of the data, our customers, and our business Design experiments and interpret the results to draw detailed and impactful conclusions. Define how our teams measure success, by developing Key Performance Indicators and other users/business metrics, in close partnership with Product and other subject areas such as engineering, operations and marketing Collaborate with applied scientists and engineers to build and improve on the availability, integrity, accuracy, and reliability of data logging and data pipelines. Develop data-driven business insights and work with cross-functional partners to find opportunities and recommend prioritisation of product, growth, and optimisation initiatives. What Youll Need: Undergraduate and/or graduate degree in Math, Economics, Statistics, Engineering, Computer Science, or other quantitative fields. 9+ years experience as a Product Analyst, Sr. Data Analyst, or other types of data analysis-focused functions Excellent understanding of statistical principles backed by an academic foundation Advanced SQL expertise Experience with either Python or R for data analysis Significant experience in setting up and evaluation of complex experiments Proven track record to wrangle large datasets, extract insights from data, and summarise learnings/takeaways. Experience with Excel and some dashboarding/data visualisation (ie Tableau, Mixpanel, Looker, or similar)
