Applied Scientist

Employer: Uber Technologies, Inc.

Job Title: Applied Scientist

Job Location: New York, New York

Job Type: Full Time

Rate of Pay: $155,000 to $186,000 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: Identify relationships and trends in data and build key algorithms behind real-time pricing, driver movement, and driver preferences products. Maximize marketplace efficiency through influencing supply and demand in real time which involves developing fundamental understandings of Uber's 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. Design and analyze experiments that provide insights to improve marketplace efficiency. Problem solve on high impact open questions and prototype cutting edge mechanisms to production and engage in large scale experimentation. Own and drive a large part of the driver movement and pricing data science roadmap in order to take Uber's products to the next level. Analyze and interpret statistical data to identify significant differences in relationships among sources of information. Work closely with various cross-functional teams (marketing managers, engineers, product managers, operations, data scientists, and analysts) to help optimize Uber's marketing practices. Successfully apply rigorous scientific methods with proficiency in data science knowledge and technical capabilities. May telecommute.

Employer will accept a Ph.D. degree in Statistics, Operations Research, Mathematics, or related field.

Position requires:

• Advertising tech (market response modeling, forecasting, attribution, targeting, and third-party data integration);
• Big data (Hadoop, Hive, or Spark);
• Python;
• Working with massive data sets using SQL;
• Machine learning and causal inference techniques; and
• Connecting technical insights to strategic decisions.