Scientist, Tech
Employer: Uber Technologies, Inc.
Job Title: Scientist, Tech
Job Location: San Francisco, California
Job Type: Full Time
Rate of Pay: $164,403 to $197,000 per year
You will be eligible to participate in Uber’s bonus program, and may be offered other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits .
Duties: Maximize marketplace efficiency through influencing supply and demand in real time which involves developing fundamental understandings of Uber’s rider and driver behavior. Create pricing algorithms that incorporate both marketplace dynamics and supply preferences. Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data. Match the best supply to the most needed demand in an effective manner. Identify relationships and trends in data and build key algorithms behind real-time pricing, rider patience, and driver preferences over riders. Design and analyze experiments that provide insights to improve marketplace efficiency. Perform problem solving on high impact open questions, prototype cutting-edge mechanisms to production and engage in large scale experimentation. Collaborate with Products, Ops, Engineering, and other Applied Scientists to own and drive a large part of the rider behavior and pricing data science roadmap to take Uber’s products to the next level. Analyze and interpret statistical data to identify significant differences in relationships among sources of information. May telecommute.
Employer will accept a Ph.D. degree in Computer Science, Operations Research, Economics, Data Science, Mathematics, Statistics, or related field; OR completion of all coursework (all but dissertation) towards a Ph.D. degree in Computer Science, Operations Research, Economics, Data Science, Mathematics, Statistics, or related field.
Position Requires
- Programming using R, Python, SQL, Java, and M3QL;
- Causal Inference and Machine Learning techniques;
- Experimentation techniques;
- Statistical analysis including data integration, market performance evaluation, factor analysis, confidence intervals, and power analysis;
- Economic forecasting, including microeconomics forecasting and financial analysis;
- Quantitative modeling using machine learning models, time-series forecasting, and causal impact analyses;
- Dashboard and data visualization tools;
- Optimization fundamentals and simulation.