Uber Data Scientist Interview
Company – Uber Technologies
Designation – Data Scientist
Experience required – 3+ years of Data Science or Analytics Experience
Compensation – We have seen candidates asking and getting 10X their year of experience. But it also depends on your current salary and counteroffers
Expertise required – Machine Learning Algorithms and their implementation
Uber Technologies, Inc. focuses on cab services, food delivery, ridesharing, and package delivery. It is based in San Francisco. Uber has over 93 million monthly active users. Office locations in India include Bangalore, Gurugram, and Hyderabad.
Products: UberX (ride-sharing mobile app) and Uber Freight (managers shipments).
Let’s dig into the rounds and questions asked in Uber Data Scientist Interview
Number of Rounds – 6
Round 1 – Case Study and 1 Coding question
Round 2 –Case Study and 1 Coding question
Round 3 – Machine Learning Case Study and Implementation Questions
Round 4 – Machine Learning Design Round
Round 5 – Code round
Round 6 – Behavioral and Hiring Manager Round
Round 1 – Case Study and Coding
This round was mostly around discussing the problem Uber is facing right now. The statement was on the lines of a high cancellation rate for the confirmed booking rides.
Uber has observed a constant increase in cancellation rate in the last few weeks, we were subject to discuss the probable reason for this behavior.
The discussion was on the points of exploratory data analysis, understanding the problem statement, formulating the null hypothesis and how would you check the actual reason for the dip using data.
Coding – You will be provided with an easy to medium-level coding question on any online coding platform.
Round 2 – Case Study and Coding
In the second round, I was asked an ML implementation and design problem along with quite a few questions on the basics of Machine Learning.
ML Problem – Design a recommendation engine based on the location of the customer. Basically, the idea was to check the location of the customer and from his/her history, suggest the location customer might be heading to.
Questions were mostly on the lines of the algorithm to choose, implementation, scalable approach, deployment, etc.
Coding problem – Medium difficulty problem from any online coding platform. If you cover all the questions from the Python question list, then these rounds can easily be sailed through
Round 3 – Machine Learning case study
Identifying and testing the right model – Almost all the rounds at Uber were very explorative with a good mixture of subjective, objective, and coding questions. In this round, I was asked to create a machine learning model to identify the rides that will be canceled by the user in the first 2 minutes. There could be multiple reasons for such activities like high price, long waiting time, allotted cab, etc.
First of all, I had to concentrate on the different variables that can be used in the model and to select the most appropriate model. I went ahead with the Logistic regression model, we had a long discussion on the type of data present and how could one extract a particular metric from raw data.
The whole interview went for 2+ hours.
Round 4 – Machine Learning Design Round
Again a complete design round, this time I had to design the most optimal and fast algorithm to show the rent of the ride. The algorithm or process needed to be super fast and scalable so that it can give results quickly and to millions of customers simultaneously.
We had to discuss the tech stack to use, the algorithm, validation, implementation, and deployment of the model
Round 5 – Code Round (SQL and Python)
Now comes a one-hour-long interview where we had to code in SQL and Python (for separate problems) and then answer the questions. In SQL we had questions in join, ranking, self join, aggregate functions, and exploding or un-nesting. For SQL, just go through the complete list of questions give in the SQL section of The Data Monk
In Python, there was 2 medium and 1 easy question. I was able to solve the easy one and partially the other two medium-level questions. There was a time constraint, but I was able to get the partially correct solution accepted.
Bonus Tip – Solve at least 50 questions from Hacker Rank ,code chef or any other coding platform
Round 6 – Hiring Manager Round
This was also a tricky round, questions were asked on your strength and weaknesses, examples of how you failed and how you succeeded in different projects, etc.
Bonus Tips – Go through Amazon’s 14 Leadership Principles once before the final round, this might help a lot
The Data Monk Interview Books – Don’t Miss
Now we are also available on our website where you can directly download the PDF of the topic you are interested in. On Amazon, each book costs ~299, on our website we have put it at a 60-80% discount. There are ~4000 solved interview questions prepared for you.
10 e-book bundle with 1400 interview questions spread across SQL, Python, Statistics, Case Studies, and Machine Learning Algorithms – Ideal for 0-3 years experienced candidates
23 E-book with ~2000 interview questions spread across AWS, SQL, Python, 10+ ML algorithms, MS Excel, and Case Studies – Complete Package for someone between 0 to 8 years of experience (The above 10 e-book bundle has a completely different set of e-books)
12 E-books for 12 Machine Learning algorithms with 1000+ interview questions – For those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package
Individual 50+ e-books on separate topics
Important Resources to crack interviews (Mostly Free)
There are a few things that might be very useful for your preparation
The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews with Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics managers, etc.
Go through the watchlist which makes you uncomfortable:-
All the list of 200 videos
Complete Python Playlist for Data Science
Company-wise Data Science Interview Questions – Must Watch
All important Machine Learning Algorithm with code in Python
Complete Python Numpy Playlist
Complete Python Pandas Playlist
SQL Complete Playlist
Case Study and Guesstimates Complete Playlist
Complete Playlist of Statistics
Thanks,
Nitin Kamal
