Uber Data Scientist Interview Questions
Uber Data Scientist Interview Questions
Company – Uber
Role – Data Scientist
Location – San Francisco, CA and India
Uber Data Scientist Interview Questions
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Round 1 -Resume Interview
Topics covered – Resume
Mode of interview – Phone
Duration – 60 Minutes
Level of Questions – Medium/Easy
A few questions from this round:
- What is a specific instance of how your predictive models influenced company decisions in a previous role?
- Name a time when your model was incorrect and how you responded.
- What makes you want to work at Uber?
- How have your previous experiences prepared you to contribute to Uber?
- What is marketing attribution?
- What is P-value?
- How can you measure the effectiveness of a machine learning model?
Round 2 – Technical Interview
Topics covered – Case Studies/ Machine Learning
Mode of interview – Phone
Duration – 45 minutes
Level of Questions – Medium
The question from this round:
- Given a heap of all recorded data for one month of rides in New York City and be asked to explain how you’d select features that measure success.
- What is the difference between supervised and unsupervised learning? What are the strengths and weaknesses of each? Which would you use for the given case study?
- Given a set of raw data, explain how you would clean the dataset to predict ride request density.
- Given a marketing data set on all marketing channel costs and resulting signups/first trips, propose a short-term marketing plan that will maximize marketing efficiency.
- What are key performance metrics for Uber? How would you rank their importance?
Round 3- Take-Home Assignment
Topics- Data science and programming skills
Mode of Interview- Home
Duration – 1 week
Level of questions- Medium
A few questions from this round:
- What factors are most correlated with user churn? Propose a business solution to counteract the highest correlated factor.
- Given a current machine learning algorithm, how would you optimize the model to provide more accurate results?
- What machine learning model would most accurately predict which driver applicants are most likely to begin driving for Uber?
- Provide a brief report that documents your assumptions, limitations, and suggestions for improving Uber’s driver acquisition.
Round 4 – Technical
Topics covered -Machine Learning
Mode: Onsite
Duration – 2 hours
Level of Questions – medium
A few questions below from this round:
- Define CLT. How is it relevant for Uber?
- How does surge pricing work? What factors should we use to determine if surge pricing is necessary?
- How would you find the lifetime value of a driver?
- How do you draw a uniform random sample from a circle in polar coordinates?
- How can we incentivize drivers to visit high traffic areas? Select and develop a model to track the effectiveness of this incentive.
Attempt all the questions given above, we will evaluate and will let you know your selection score
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