If you are a talented data scientist, Uber wants you to help make its services, like Rides and Eats, more useful. Data scientists at Uber are very important because they look at huge amounts of data to figure out how to solve difficult logistical problems.
The company has good benefits and competitive pay, which makes it a good place to work if you want to improve your data science skills and move up in your career.
This guide gives you an overview of the Uber data scientist interview process. It includes frequently asked questions and useful advice to help you with your application.
Preparing for your Uber data analyst or data scientist interview? You’ve landed in the right place. This guide will equip you with the knowledge and strategies to ace your interview and secure your dream role at this global tech giant.
Why Uber?
Uber’s innovative business model revolves around data analysis and data science. As a potential data analyst or data scientist, you’ll be entrusted with the crucial task of crunching numbers, interpreting trends, and generating insights that drive strategic decisions. Your analytical prowess and problem-solving acumen will play a pivotal role in helping Uber maintain its competitive edge in the fast-paced ride-hailing market.
What to Expect
Interview Process
- Preliminary Screening: A hiring manager will assess your work experience and cultural fit. Prepare responses and showcase your past projects.
- Take-home Assignment (Optional): Complete an SQL problem, qualitative analysis, and an applied modeling case study within a week.
- Technical Screening: A series of video call and in-person interviews will evaluate your SQL expertise, machine learning knowledge, product sense, and behavioral traits.
Commonly Asked Questions:
1. How would you use data analytics to optimize Uber’s dynamic pricing model?
2. Could you show me an example of a predictive model you’ve made and explain how it could be used to make ride allocation better during rush hours?
3. Describe a time when you explained a complex technical problem to a non-technical person.
4. Why do you want to join Uber?
5. How would you avoid bias while deploying solutions?
6. Tell me about a conflict you’ve had with a co-worker.
7. What would you change about Uber?
8. Write a query to find out how long each New York (NY) commuter spends on the road each day and how long all New York commuters spend on the road each day.
9. What are Type I and Type II errors? Which one is worse?
10. Let’s say we want to improve the matching algorithm for drivers and riders for Uber. The engineering team has added a new column to the drivers table called weighting. Write a query to perform a weighted random selection of a driver based on driver weight.
11. What assumptions would you make while setting up an A/B test?
12. Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Give an example of the tradeoffs between the two.
13. Is the LSTM model good for long-term forecasting?
14. How would you design an incentive scheme for drivers that would encourage them to go to areas of the city with high demand?
15. Describe how you would price rides if you were to do it from scratch.
16. Given a table of cars with columns id and make, write a query that outputs a random manufacturer’s name with an equal probability of selecting any name.
17. You have been given 90 days of ride data. How would you use the ride data to project the lifetime of a new driver on the system? What about the lifetime value of the driver?
18. Uber is considering introducing an “Uber Pet” service, where riders can bring their pets along for a ride for an additional fee. How would you assess the feasibility of this service?
19. Let’s say we want to build a model to predict the time a restaurant spends preparing food from the moment an order comes in until the order is ready. What kind of model would we build, and what features would we use?
20. What’s the relationship between PCA and K-means clustering?
21. We want to determine if Uber Eats has a net positive value for the company. How would you measure its success?
Additional Tips:
- Research Uber’s business model and challenges.
- Brush up on technical skills like statistics, machine learning, and data manipulation.
- Review past projects and case studies.
- Practice problem-solving and behavioral questions.
- Prepare questions for the interviewer.
By following these tips and utilizing the resources provided, you’ll be well-equipped to impress your interviewers and land your dream job at Uber.
Remember, your success hinges on demonstrating your technical expertise, problem-solving skills, and passion for Uber’s mission.
Best of luck!
How to Prepare for a Data Science Interview at Uber
Here are some tips to help you excel in your interview.
Describe a time when you explained a complex technical problem to a client who didn’t have a technical background.
Excellent communication is essential since cross-functional collaboration with non-technical teams can be expected.
How to Answer
Focus on a specific instance where you broke down a technical issue for a non-technical audience. Use the STAR method of storytelling. Talk about the specific problem you were faced with, the task you chose, the action you took, and the outcome of your efforts.
Example
“In a previous job, I had to explain a complicated cloud integration problem to a client who didn’t know much about cloud computing.” I compared the process to merging different departments within a company, each with its unique processes and data. I made sure to use minimal technical jargon. This helped the client grasp the challenges of the problem. ”.
Interviewers will want to know why you chose the data scientist role at Uber. They want to evaluate your passion for the company’s culture and values.
How to Answer
Demonstrate knowledge of Uber’s work, culture, and the distinct opportunities that attract you to the company. Be honest and specific about how Uber’s offerings align with your career goals.
Example
“If I worked at Uber, I could be on a team that encourages new ideas, supports learning, and has a daily impact on millions of lives.” Uber’s creative approach to real-world transportation problems, its global reach, and the chance to work with diverse teams all interest me. ”.
