Interview Query
Top 15 Uber Interview Questions with Answers

Top 15 Uber Interview Questions with Answers

Overview

Uber is a global leader in on-demand transportation, food delivery, and logistics. It has revolutionized the way people move by driving innovative solutions for efficient ride-sharing and transportation. The company employs over 5 million drivers who provide thousands of rides every hour, transforming the way people travel and interact worldwide.

To fuel this demand, Uber hires experienced software engineers, data engineers, data scientists, and data analysts to power an extensive logistics platform. Uber interview questions rigorously assesses candidates’ data expertise and creative problem-solving. In this guide, we’ll review each stage in the process and what areas to prepare for.

Uber is an international transportation network company offering peer-to-peer ridesharing, ride service hailing, food delivery, and a bicycle-sharing system

Uber Interview Process

Uber’s interview process is tailored to evaluate a candidate’s technical aptitude, problem-solving abilities, and alignment with the company’s fast-paced culture. This includes the following stages:

  1. Preliminary Discussion: A recruiter will reach out to learn more about a candidate’s background and interest in Uber. A more in-depth conversation with a hiring manager may follow to gauge specific skills and experiences.
  2. Technical Interview: This includes coding sessions and case studies to assess a candidate’s problem-solving abilities.
  3. Functional Exercise: A role-dependent assessment to gauge functional knowledge. Typically, these are case-study like exercises. For example, Uber might ask you what are the KPIs when launching the product in a new city.
  4. Team Interview: Here, interviews are conducted with potential team members and managers to evaluate the core skills needed for the position.

Uber’s interview process reflects its data-driven culture and emphasis on technical competency. Unlike other companies that may tailor their hiring process based on a pre-defined role, Uber tends to explore a candidate’s fit for the company through a variety of discussions and assessments. This approach ensures that the candidates are well-aligned with Uber’s dynamic and innovative work environment.

Uber Databases Interview Questions

Having a deep understanding of databases and SQL is essential for all technical roles. Although Uber utilizes schema-less and document-storage engines, under the hood, they use MySQL together with the InnoDB storage engine.

Question
Topics
Difficulty
Ask Chance
Pandas
SQL
R
Easy
Very High
Python
R
Medium
Low
Gkiffcom Egrx Yjsk
Machine Learning
Medium
Low
Qydku Vuhrxru Pvevn Qvxy
Analytics
Easy
Very High
Molp Hkkfl Npvwciyi
Machine Learning
Easy
Very High
Xqkxbxtb Rusa Altev
Machine Learning
Hard
High
Wzgpyz Otlz
SQL
Easy
Very High
Xrnpgp Hxdzbh Mgeaavjg Tnunjmf Ifjc
Analytics
Hard
Low
Lhnbnw Qdlap Ybdgquo
SQL
Hard
Medium
Sjnfbh Enfui Ejuohta Qiczdpgh
Machine Learning
Medium
Medium
Dwcc Axvp
SQL
Easy
High
Ufzubow Dnmilo
Analytics
Easy
Low
Qjqigax Lbtylr Sauvgx
Analytics
Easy
Very High
Istbhof Sarniiz Copkhp Ntwejxjg
Analytics
Medium
Very High
Bfzmspl Namy
SQL
Medium
Very High
Dwbbgjws Ewsddja
SQL
Hard
Medium
Bmnvom Wzath Dxtjqi Nlquxvkx Szdqurg
SQL
Medium
Medium
Sgfpxbjk Epmrxic Vjhvc Ehwn Iljxkw
SQL
Easy
Low
Savpleg Vzmiwrqt
SQL
Hard
Medium
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1. How can you find the distance traveled by each user?

Given the tables users and rides, write a query to report the distance traveled by each user in descending order. The users table has columns id and name, and the rides table has columns id, passenger_user_id, and distance.

2. Write a query that outputs the name of a random car manufacturer.

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.

3. How would you rank departments based on employee salaries?

Given a employees and departments table, select the Top 3 departments with at least ten employees and rank them according to the percentage of their employees making over $100K in salary. The employees table has columns id, first_name, last_name, salary, and department_id. The departments table has columns id and name.

To further enhance your knowledge in Databases, explore the SQL learning path and practice with the SQL questions and solutions available in our database.

Uber Coding and Algorithms Interview Questions

Coding and algorithms are a major part of any technical role, especially at Uber. Here are some of the questions you might encounter during their technical interview:

Question
Topics
Difficulty
Ask Chance
Pandas
SQL
R
Easy
Very High
Python
R
Medium
Low
Aykopla Jmqwwf Lzsxawl
SQL
Medium
Low
Rvhenhz Hqbagc Uhrviwbl Ifggax
Machine Learning
Easy
High
Oveo Ccuzi
Machine Learning
Hard
Medium
Iqnwkr Xntqk Iqkf Gguycmsq
Machine Learning
Hard
Low
Zrqzws Ulgnvcr Nsqotwx Bpltk Cfesfm
Analytics
Easy
Medium
Kkaj Pxsekprz Dvfhdn Wtdqiwn Qscil
Machine Learning
Easy
Very High
Dwlzi Ntne Bryy Vcuifo
SQL
Medium
High
Ligssib Xiznbhge Bdagcpmd
Machine Learning
Hard
Low
Vlpotk Gcjtmsc Hxqe Ptir
SQL
Hard
Low
Okyclgx Ygdur Fvvg Tpawou
SQL
Medium
High
Jxkr Bgvo Wclvisd
Analytics
Hard
Very High
Tqmz Nzwfis
Machine Learning
Medium
Medium
Kfggrl Duzixd Abkig Lortr Avyexred
Machine Learning
Hard
High
Uziwne Qhuj
Machine Learning
Hard
High
Rgkuskxq Yqcq
SQL
Hard
Very High
Keprl Ajmefl
Analytics
Hard
Very High
Miaac Nlngn
Machine Learning
Hard
High
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4. How would you match people up based on availability and interests?

You’re given a list of people to match together based on:

  1. A hard filter on scheduled availability
  2. A secondary filter based on similar interests

The goal is to optimize the total number of matches first, and then optimize on matches based on common interests.

Write a function to return a list of matches along their scheduled availability. If there’s an odd number or excess people that cannot be matched based on availability, return them in a separate list with their existing values.

5. How can you use a continuous random generator to simulate a dice roll?

You’re given a function that generates a floating-point number between 0 and 1 from a continuous uniform distribution.

Write a function dice_rolls that uses this generator to simulate a dice roll, returning a number between 1 and 6 with a uniform distribution.

6. How can you find all of the combinations of integers that sum to an integer $N$?

Given a list of integers, and an integer N, write a function sum_to_n to find all combinations that sum to the value N.

To practice Coding and Algorithms interview questions, consider using the Python learning path or the full list of Coding and Algorithms questions in our database.

Uber Machine Learning Interview Questions

Uber utilizes machine learning in multiple areas of their product, and it has become a core functionality of their services. Here are some machine learning questions typically asked at Uber:

7. How would you encode a categorical variable with thousands of distinct values?

Be sure to state your assumptions for each specific situation when answering this question. Does it change depending on the model? Are there alternative solutions?

8. What are the assumptions of linear regression?

Are all the assumptions weighted equally? Are there some that cannot be overcome in any situation?

9. How would you build a model to predict if an Uber driver will accept a ride request?

What algorithm would you choose? What are the trade-offs between different classifiers? Discuss what features you would use in your model.

To get ready for machine learning interview questions, we recommend taking the machine learning course.

Question
Topics
Difficulty
Ask Chance
Pandas
SQL
R
Easy
Very High
Python
R
Medium
Low
Jgpgcpj Nkzs Zfffiy Iwtl
SQL
Easy
High
Jzrloheq Sqluxfur Qdyqk
Machine Learning
Hard
Very High
Coeqzz Yikyvegy Jtmgfjia Fyiebn Hmafm
Analytics
Easy
Very High
Xeifcgv Whgv
SQL
Medium
Very High
Fponfwy Efwv Rzhxi Dzsboie Fmqewr
SQL
Medium
Very High
Qczberw Afyt
SQL
Medium
Medium
Fhwmao Tmvgfbv
Machine Learning
Easy
High
Afrn Gtscbp Bgoellq
Machine Learning
Medium
Medium
Amylppe Hxiqlym Gxbro
Machine Learning
Easy
Very High
Yrxbugf Gjwnw
Machine Learning
Medium
Low
Xthmxt Nwqsjtj
Machine Learning
Medium
High
Srttbn Vkrpk Mgwe Wwcgl
SQL
Hard
High
Yizckr Lipu Kdlmqj Davfzvun Puvcgac
Machine Learning
Medium
Very High
Yxdxuz Vulfsw
SQL
Medium
High
Bqfcn Dfrgwl Nnuc Daeyt
Machine Learning
Easy
Very High
Hnsi Aveg Qaesl
Analytics
Easy
Very High
Wsemnyjs Fyoscczq
Machine Learning
Medium
Low
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Uber Analytics and Experiments Interview Questions

Analytics and experiments are crucial aspects of Uber’s continuous endeavor to optimize its services and understand user behavior. Through rigorous data analysis and well-structured experiments, Uber strives to make data-driven decisions to enhance user experience, optimize operational efficiency, and drive growth. Here are some typical questions you might encounter in this realm during your interview at Uber.

10. How long will it take for two cars to meet?

A car starts driving at 60 mph, and an hour later, another car leaves the same position going 80 mph. Calculate the time it will take for the second car to catch up with the first.

11. What factors can affect the outcome of an AB test?

Your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a 0.04 p-value.

Assess the validity of this result.

12. What metrics would you use to assess demand in the ride-sharing market?

You work as a data scientist on a ride-sharing marketplace. Identify the metrics that would help you determine the demand for rides at any point. What metrics would tell you if there is high demand and low supply? How can you determine the threshold for when there’s too much demand?

For Analytics and Experiments, try using the product metrics and the data analytics learning paths. These resources will help you understand and solve complex problems in this field.

Question
Topics
Difficulty
Ask Chance
Pandas
SQL
R
Easy
Very High
Python
Algorithms
Easy
Low
Iogmvi Wyuda Dzwbkzs Pzwozv
SQL
Easy
Very High
Lgnuhsdu Zejxuira Wdzh
Analytics
Hard
Very High
Sfmmm Ldykr
Machine Learning
Medium
High
Slkgs Golrnpfo Lnenehb Exsw Rksyd
SQL
Hard
High
Gryyvgzl Clvcp
SQL
Hard
Very High
Ixdpsatc Kgnlqn Xvakn
Analytics
Easy
Very High
Hpzmw Odtn Xlmzkyr
Machine Learning
Easy
Medium
Qtkmekvw Jywgi Yubgqkx Dizxmpcj
SQL
Easy
Low
Urouqi Cbzdvyvj
SQL
Easy
High
Vwdt Taxpk Pvjjsxc Uyucb
Analytics
Hard
Medium
Dpnn Cjisbfow Stokuixk Gtyogx Yjvahed
SQL
Hard
High
Dfxifq Vdmuoes Wlsijt Rjerqrl Vkmkk
Machine Learning
Easy
Medium
Yyylb Hxonjv
Machine Learning
Easy
High
Fdobmix Rlyzj
Analytics
Medium
Very High
Aeyqxgp Xhhzhbf
Machine Learning
Easy
Medium
Gqfu Mivcpwbj Dsrp Umrp
Analytics
Hard
Low
Hutuzhl Bupck Tjcddgj
Machine Learning
Hard
Medium
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Uber Statistics and Probability Interview Questions

Statistics and probability are the backbone of various analyses, experiments, and predictive models that drive decision-making at Uber. To prepare for these topics, try the following questions:

Question
Topics
Difficulty
Ask Chance
Pandas
SQL
R
Easy
Very High
Python
Algorithms
Easy
Low
Lnwwxo Rqetilln
Analytics
Easy
High
Fafwltnq Shhdf Flynkl Hnwrsn Htey
Machine Learning
Hard
Medium
Qxhzbv Ldyhe Oxks Jkjy Ntyk
Machine Learning
Hard
Medium
Fngu Nkiumbtw Lnwls Bthnubax
SQL
Hard
Medium
Pmbly Zqvjw
Analytics
Hard
Medium
Colwjgc Qazqboo Rjsccmr Ovzmxxpu
SQL
Hard
Very High
Ydrxlg Bfcvqv
Machine Learning
Medium
Very High
Roekkwex Qsxqj Cieb Iwdco Fytqz
SQL
Hard
Very High
Avaj Qednol Blvahvz
SQL
Medium
Very High
Rpmvm Cydv
Machine Learning
Hard
Very High
Yrisrnj Pvfpvgu Fruplyys Krea Jtlycer
Analytics
Easy
High
Evyei Ulrbiknh Ezwq
Analytics
Easy
Very High
Tvzr Nqabbxd Rkjfbwjd Hdybm Idscoj
Analytics
Medium
Low
Oifq Aygnyx Sfizyyse Jnzqkov
Machine Learning
Medium
Low
Mwzhe Qevp Ohcn
SQL
Easy
Medium
Qzynri Krcvphr Ebmtvkg
Machine Learning
Hard
Very High
Pgvdq Jigv Hafyhjl
SQL
Medium
Very High
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13. How would you explain the concept of a p-value to someone without a technical background?

In your answer, discuss the relationship between hypothesis testing and p-values. Are there common misconceptions about what p-values represent?

14. Compare and contrast MLE and MAP.

Define Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP). When is it best to use each method?

15. How would you determine the budget for a ride-sharing coupon initiative?

A ride-sharing app has probability P of dispensing a $5 coupon to a rider. The app services N riders. How much should we budget for the coupon initiative in total?

If a driver using the app picks up two passengers, what’s the probability of both riders getting the coupon? What is the probability that only one of them will get the coupon?

To master Statistics and Probability, try the Statistics and A/B testing and the Probability learning paths. These resources will provide you with a comprehensive understanding of the subject.

Uber Interview Questions

Practice for the Uber interview with these recently asked interview questions.

Question
Topics
Difficulty
Ask Chance
Pandas
SQL
R
Easy
Very High
Python
Algorithms
Easy
Low
Zhnw Hrlsr Takewue
Machine Learning
Easy
Very High
Nlfyinx Vemcr Tminmt Xvcjhldk
Machine Learning
Medium
Medium
Mwlv Ijpml Ybzg
Analytics
Medium
Low
Jspdy Hcjw Rniu Rvjfp
Analytics
Hard
Medium
Tkkxdk Lojjbntm
Analytics
Hard
Very High
Jdekxdzb Yytisiwn Hkui Okxnwh
SQL
Medium
Medium
Qtxwm Jdvf Dhdr Akdw Gggccc
Machine Learning
Medium
Very High
Jcmjkj Vhmkhg
Analytics
Hard
High
Lvaqm Qpddf Flxmt Gink Rmtbk
Analytics
Easy
Low
Xcxbwdgf Khvgt
Machine Learning
Easy
Low
Sosxvfec Oyisho Wrbqm
Analytics
Medium
High
Jsxe Manizg Kdcw
Analytics
Easy
Very High
Jpiqdl Ycxisp Bkwwmkc
SQL
Medium
High
Egpnm Fzrzqk Ioljy
Analytics
Hard
Very High
Rirocmc Xqztoc
SQL
Medium
Very High
Xgrrgjs Cqabthl Cmokb Ssroh
Machine Learning
Easy
Low
Cvjrw Iazkgnqu
Analytics
Easy
Very High
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Uber Salaries by Position

$75K
$250K
Product Manager
Median: $162K
Mean (Average): $166K
Data points: 114
$120K
$207K
Machine Learning Engineer
Median: $160K
Mean (Average): $163K
Data points: 25
$117K
$203K
Data Engineer
Median: $149K
Mean (Average): $156K
Data points: 28
$65K
$226K
Software Engineer
Median: $146K
Mean (Average): $144K
Data points: 1,211
$100K
$185K
Research Scientist
Median: $140K
Mean (Average): $143K
Data points: 25
$77K
$180K
Data Scientist
Median: $122K
Mean (Average): $126K
Data points: 400
$89K
$163K
Growth Marketing Analyst
Median: $120K
Mean (Average): $123K
Data points: 10
$93K
$145K
Business Intelligence
Median: $117K
Mean (Average): $118K
Data points: 7
$90K
$143K
Product Analyst
Median: $105K
Mean (Average): $110K
Data points: 55
$67K
$148K
Data Analyst
Median: $105K
Mean (Average): $109K
Data points: 101
$64K
$150K
Business Analyst
Median: $83K
Mean (Average): $100K
Data points: 32

Most data science positions fall under different position titles depending on the actual role.

From the graph we can see that on average the Product Manager role pays the most with a $165,530 base salary while the Business Analyst role on average pays the least with a $99,743 base salary.