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Uber Data Science Intern Interview Questions and Answers
Uber Data Science Intern Interview Experiences
1 interview found
Anonymous
I was interviewed in Dec 2021.
Round duration - 60 minutes
Round difficulty - Easy
Timing: 8:00-9:00 PM
There was 1 MCQ and 3 SQL queries. The platform was easy to use and navigate.
Round duration - 30 minutes
Round difficulty - Medium
Timing: 11 AM-11:30 AM
The interviewer was very kind and helpful. He helped me by giving hints whenever I was stuck.
(1 Question)
Round duration - 30 minutes
Round difficulty - Medium
Timing: 12:15 PM to 12:45 PM.
The interviewer was very helpful and friendly. He was more interested in my approach rather than the final answer.
- Q1. What new feature would you like to add to Uber?
Round duration - 30 minutes
Round difficulty - Hard
Timing: 3:00PM to 3:30PM
This was Hiring Manager round. He asked me case study type of questions.
Interview Preparation Tips
Tip 1 : Practice SQL and python coding questions using online coding platforms.
Tip 2 : Get in-depth theoretical knowledge about all the machine learning algorithms.
Tip 3 : Strengthen your statistics understanding.
Tip 1 : Highlight your skills properly
Tip 2 : Have thorough understanding about everything written in the resume
Top trending discussions
Interview questions from similar companies
Basic DSA questions will be asked Leetcode Easy to medium
(2 Questions)
- Q1. BERT vs LSTM and their speed
- Ans.
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.
BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.
For exa...
- Q2. What is multinomial Naive Bayes theorem
- Ans.
Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
It is commonly used in text classification tasks, such as spam detection or sentiment analysis.
It is suitable for features that represent counts or frequencies, like word counts in text data.
It calculates the probability of each class given the input features and selects the class with the
Skills evaluated in this interview
Selected
I applied via Company Website and was interviewed before Dec 2023. There were 3 interview rounds.
Categories of the CAT exam include Quantitative Aptitude, Verbal Ability, Data Interpretation and Logical Reasoning, and Graphical questions.
Medium level. Focus on SQL subqueries application.
(2 Questions)
- Q1. Statistical questions related to different hypothesis testing
- Q2. Questions related to different machine learning model
(2 Questions)
- Q1. Tell me about your projects?
- Q2. How do you approach the project if you are using logistic regression model?
- Ans.
Approach involves data preprocessing, model training, evaluation, and interpretation.
Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.
Split the data into training and testing sets.
Train the logistic regression model on the training data.
Evaluate the model using metrics like accuracy, precision, recall, and F1 score.
Interpret the model coefficients to under...
(2 Questions)
- Q1. What are you future goals?
- Q2. What would you do if your interested field doesnt have any work in the company?
- Ans.
I would seek opportunities to apply my skills in related fields within the company.
Explore other departments or teams within the company that may have projects related to my field of interest
Offer to collaborate with colleagues in different departments to bring a new perspective to their projects
Seek out professional development opportunities to expand my skills and knowledge in related areas
Skills evaluated in this interview
Namitha Cherian
I applied via Referral and was interviewed before Oct 2023. There were 2 interview rounds.
(1 Question)
- Q1. Explain past projects
(1 Question)
- Q1. Explain resume points
- Ans.
Resume points are concise descriptions of your work experience, skills, and achievements listed on your resume.
Resume points should be clear, specific, and quantifiable.
Use action verbs to start each point, such as 'developed', 'implemented', 'analyzed'.
Include relevant metrics or results to demonstrate impact, such as 'increased sales by 20%' or 'reduced processing time by 30%'.
SQL - shared question on the screen and asked to code in SQL
(1 Question)
- Q1. Nth highes salary
Selected
I was interviewed in Dec 2023.
English, number system, grammar
Python , data science, machine learning
Python machine learning, natural language precossing
(4 Questions)
- Q1. Python , Data Science, SQL
- Q2. What is python basics, libraries
- Ans.
Python basics include syntax, data types, and control structures. Libraries like NumPy, Pandas, and Matplotlib enhance data analysis and visualization.
Python basics cover syntax, variables, data types, and control structures.
NumPy is a library for numerical computing, providing powerful array operations.
Pandas is a library for data manipulation and analysis, offering data structures like DataFrames.
Matplotlib is a libr...
- Q3. Data science algorithms , and theory
- Q4. Sql querys, cluases
Indian environment, village, college days
(1 Question)
- Q1. Python, data science
(2 Questions)
- Q1. Basic Recommendation System Questions
- Q2. A B Testing Questions
I applied via Referral and was interviewed in Nov 2024. There was 1 interview round.
(2 Questions)
- Q1. SQL and pandas coding
- Q2. Resume projects deep dive
Interview Preparation Tips
Selected
I applied via Company Website and was interviewed before Dec 2023. There were 3 interview rounds.
Categories of the CAT exam include Quantitative Aptitude, Verbal Ability, Data Interpretation and Logical Reasoning, and Graphical questions.
Medium level. Focus on SQL subqueries application.
(2 Questions)
- Q1. Statistical questions related to different hypothesis testing
- Q2. Questions related to different machine learning model
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