1. Create a quantitative pickup quality metric using attributes derived from the passive, active, and third-party signals available to Uber. Discuss why you believe your selected attributes represent a robust pickup quality metric. What weights would you assign to the features you chose for your pickup model?

2. Based on your pickup quality metric, what actions can Uber operators take to improve the pickup experience?

3. How would you improve the pickup experience at venues such as sporting events and concerts, which typically see temporary surges in demand for Uber rides, as well as temporary parking restrictions and traffic congestion?

4. In Exhibit 7 of the case (page 21; also shown in right to this slide), what would you have proposed as Data Science Leads for the 3 Data Science steps in Uber (highlighted in blue)?

1. Create a quantitative pickup quality metric using attributes derived from the passive, active, and third-party signals available to Uber. Discuss why you believe your selected attributes represent a robust pickup quality metric. What weights would you assign to the features you chose for your pickup model?

2. Based on your pickup quality metric, what actions can Uber operators take to improve the pickup experience?

3. How would you improve the pickup experience at venues such as sporting events and concerts, which typically see temporary surges in demand for Uber rides, as well as temporary parking restrictions and traffic congestion?

4. In Exhibit 7 of the case (page 21; also shown in right to this slide), what would you have proposed as Data Science Leads for the 3 Data Science steps in Uber (highlighted in blue)?