Top 22 Uber Data Scientist Interview Questions + Guide in 2024

Uber values talented data scientists who can contribute to enhancing the effectiveness of its offerings, such as Rides and Eats. Data scientists at Uber play a crucial role in analyzing vast amounts of data to solve complex logistical challenges.

The company offers competitive salaries and benefits, making it an attractive workplace for enhancing your skills and advancing your career in data science.

This guide provides an overview of the interview process, featuring commonly asked Uber data scientist interview questions and practical tips to assist you on your application journey.

Most participants rate Uber’s data science interview somewhere between medium and hard, but if you’ve done your homework and prepared carefully, you won’t need to worry. Below are the stages you can expect.

Step 1: Preliminary Screening

After you apply, a hiring manager will contact you to get a sense of your work experience and cultural fit. Prepare some responses and study your past projects.

Common questions asked in this stage, according to Uber’s Careers page:

1. Describe your technical experience.
2. Why are you interested in joining Uber?
3. What are you looking for in a technical role?
4. What motivates you in your career?

You might be asked some high-level technical questions at this stage, too, such as: how would you explain p-value to a non-technical person?

Step 2: Take-home Assignment

This round is sometimes skipped, but candidates are usually given an assignment to complete within a week. It consists of:

1. An SQL problem
2. Qualitative analysis, for example, a metrics evaluation problem
3. An applied modeling case study

Step 3: Technical Screening

You will next be invited to a series of technical interviews over video calls and in person. There may be up to 5 or 6 rounds. The interviews will assess your SQL expertise, machine learning knowledge, product sense, and behavioral traits.

Here are some interview tips from Uber’s Careers page:

1. Prepare an elevator pitch about your background and why you are perfect for the role.
2. When talking about past projects, elaborate on your contribution.
3. It never hurts to go back to fundamental concepts.
4. Practice, practice, practice!

The Uber data scientist is expected to have a solid grasp of SQL, coding, machine learning algorithms, product sense, and A/B testing. Try to practice questions in the context of Uber’s business and products, as your real-world problem-solving ability will be assessed.

Read on for our handpicked practice problems for the Uber interview. For best results, try answering the question yourself before looking at our hints or clicking on the solution!

1. 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. Discuss the Specific situation you were challenged with, the Task you decided on, the Action you took, and the Result of your efforts.

Example

“In my previous role, I was once tasked with explaining a complex cloud integration issue to a client unfamiliar with 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.”

2. Why do you want to join Uber?

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

“Working at Uber would give me a chance to be part of a team that values innovation, promotes learning, and impacts millions of lives daily. I’m intrigued by Uber’s innovative approach to solving real-world transportation challenges, its global impact, and the opportunity to work with diverse teams.”

3. How would you avoid bias while deploying solutions?

Given Uber’s focus on diversity and inclusion, the company would want to understand how well-versed you are in avoiding algorithmic prejudices.

How to Answer

Describe an instance where you identified bias in a dataset or analysis process, and highlight the impact of your actions on the project outcomes.

Example

“In a previous project to enhance loan approval algorithms for a fintech company, I looked at historical trends and identified a bias where applicants from certain zip codes were less likely to be approved. We then re-evaluated our data sources and model assumptions and made the approval process more equitable. We did this by incorporating a broader set of financial health indicators and removing zip code as a determinant factor.”

4. Tell me about a conflict you’ve had with a co-worker.

This question checks your emotional intelligence and conflict resolution skills—both critical to being a good team player.

How to Answer

Describe a conflict in which you played a role in finding a mutually beneficial outcome. Highlight what you learned from the experience, showing your willingness to adapt and grow.

Example

“I once had a conflict with a co-worker over prioritizing project features. To resolve it, I set up a one-on-one to discuss our viewpoints and come to an agreement. We decided to consult other team members and gather more user data to make an informed decision. This experience helped me appreciate the importance of empathy and flexibility in teamwork.”

5. What would you change about Uber?

This is an excellent behavioral question as it simultaneously assesses your critical thinking and understanding of Uber’s products and business objectives.

How to Answer

Reflect on your experience with the app, identifying any features or processes that seemed cumbersome or areas where Uber could update their technology for better service. Your answer should show you understand the user’s perspective and can think strategically about product development. Show that you are aware of Uber’s business strategy and goals and align your solutions accordingly.

Example

“One area I believe could improve is integrating real-time urban mobility data to optimize route planning for rides and deliveries. For example, incorporating live traffic updates, public transit schedules, and pedestrian flow patterns could enhance the app’s navigational algorithms to help reduce wait times. These improvements would provide more eco-friendly route options by suggesting rideshare opportunities or integrating with public transit options. Learning from the recent redesign that aimed at simplification and personalization, this feature could further personalize the user experience. This could start with a pilot in densely populated cities, using a phased approach to refine algorithms.”

6. Write a query to find the average commute time (in minutes) for each commuter in New York (NY) and the average commute time (in minutes) for all commuters in New York.

This question tests your skill in applying commonly required SQL functions to Uber’s business problems.

How to Answer

Calculate average times using SQL aggregate functions such as AVG() and employ conditional aggregation or a subquery to obtain the overall average for comparison.

Example

“I’d calculate the average commute time for each commuter by finding the difference in minutes between start_dt and end_dt for each ride and then the average of those times per commuter. I would also include a subquery to calculate the overall average commute time for all New York commuters, presenting these two pieces of information side by side for each commuter. This approach provides a benchmark for comparison.”

7. What are Type I and Type II errors? Which one is worse?

Understanding Type I and Type II errors is necessary to evaluate the performance of predictive models.

How to Answer

Consider the consequences of each error in a scenario relevant to Uber’s operations to determine which one could be worse.

Example

“A Type I error, or a false positive, occurs when a hypothesis is incorrectly rejected when it is true. For example, if Uber’s algorithm incorrectly flags a legitimate ride request as fraudulent, that’s a Type I error. A Type II error, or a false negative, happens when we fail to reject a false hypothesis. For instance, if Uber’s algorithm fails to detect a fraudulent transaction. In this context, a Type I error could lead to customer dissatisfaction, whereas a Type II error would result in financial loss. Type II errors might be considered worse due to the direct financial implications, but it’s crucial to balance both to maintain trust and operational efficiency.”

8. 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. It contains a weighted value, which they hope will lead to better matching. Given this table of drivers, write a query to perform a weighted random selection of a driver based on driver weight.

This kind of problem directly relates to how Uber could experiment with features to enhance service quality.

How to Answer

Highlight how you would adjust driver weights to reflect their selection probability, and mention the SQL functions you would employ to achieve this.

Example

“The process involves two key steps: adjusting the drivers’ weights so they reflect the probability of selection and then picking a driver based on these probabilities. First, I’d calculate the sum of all weights in the drivers’ table to understand each driver’s relative weight. I’d then generate a random number and use it to select a driver by comparing this number to the cumulative distribution of weights. This method ensures that drivers with higher weights have a proportionally greater chance of being matched.”

9. What assumptions would you make while setting up an A/B test?

A deep understanding of A/B testing is essential at Uber, where data science teams are constantly evaluating changes in the platform.

How to Answer

Describe the fundamental assumptions critical to the validity of A/B testing, such as randomization, independence, and equal distribution of variables other than the test variable. Emphasize the importance of these assumptions in ensuring unbiased results.

Example

“I’d start by ensuring that participants are randomly assigned to either the control or test group to mitigate selection bias. I’d also assume that the outcomes from one participant are independent of another, meaning that one user’s experience doesn’t affect another’s—essential in Uber’s context, where user experiences can be quite varied. Another assumption would be that, aside from the intentional differences applied in the test and control groups, all other conditions affecting the outcome are equally distributed between them. This might include day of the week, time of day, or specific geographic considerations. Lastly, I’d assume we have a large enough sample size to detect a meaningful difference between the groups.”

10. 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.

This question checks your understanding of ensemble methods and ability to choose the appropriate algorithm based on a project’s specific needs.

How to Answer

Highlight the key differences and provide relevant examples of when you would employ each method.

Example

“Bagging, like in a random forest, is robust against overfitting and works well with complex datasets. However, it might not perform as well when the underlying model is overly simple. Boosting, exemplified by algorithms like XGBoost, often achieves higher accuracy but can be prone to overfitting, especially with noisy data. It’s also typically more computationally intensive.”

11. Is the LSTM model good for long-term forecasting?

Understanding the strengths and limitations of various forecasting models is essential for making accurate predictions over different time horizons. It’s relevant for companies like Uber, which rely on predictive modeling for demand forecasting, surge pricing algorithms, and optimizing supply-chain logistics.

How to Answer

Discuss the capabilities of LSTM models in capturing long-term dependencies in sequential data compared to traditional time series forecasting. Make sure you briefly mention the limitations.

Example

“I believe LSTMs are quite effective for forecasting problems where the data has long-term dependencies, as they retain information for long periods. This is useful for demand forecasting, where patterns can span weeks and are influenced by holidays or local events. However, for very long-term forecasting, the performance of LSTMs might not be ideal. This is because the further out we try to predict, the more uncertainty there is, and LSTMs, like any model, can struggle with the accumulation of prediction errors over time.”

12. How would you design an incentive scheme for drivers that would encourage them to go to areas of the city with high demand?

This question gauges your ability to apply data science principles to real-world business problems, like how to balance supply and demand dynamically.

How to Answer

Outline a strategy that involves analyzing historical data to identify demand patterns, setting clear objectives for the incentive scheme, and considering both short-term and long-term incentives. Discuss how you’d measure the scheme’s success.

Example

“I’d start by looking at Uber’s historical data to identify when and where demand peaks typically occur. I’d then propose dynamic incentives that increase earnings for trips into these high-demand areas. These incentives could be structured as additional per-trip bonuses, higher rates during certain hours, or rewards for completing a set number of rides in the targeted areas within a specific timeframe. It’s also important to communicate clearly with drivers so that they understand the benefits of participating.

Moreover, I’d monitor the effectiveness of this scheme through KPIs such as the number of drivers in high-demand areas during targeted times, customer wait times, and overall customer and driver satisfaction. Adjustments to the incentives might be necessary based on these metrics to ensure we’re meeting our target of balancing supply and demand properly.”

13. Describe how you would price rides if you were to do it from scratch.

A core challenge for Uber is balancing profitability with customer satisfaction; pricing analytics is, therefore, a key skill set that the company values.

How to Answer

Outline a framework incorporating multiple factors: distance, demand-supply balance, time of day, special events, and operational costs. Highlight data analysis to predict demand patterns and adjust prices in real time.

Example

“I’d first introduce a base fare considering distance, time, and base operational costs. I’d incorporate a dynamic component adjusting for real-time demand and supply; for example, prices could increase in areas with high demand but low driver availability. I’d also factor in time-of-day variations, with peak hours having higher rates. Special circumstances like holidays or large local events would also trigger adjustments. Importantly, the model would be transparent to users, explaining why prices might be higher at certain times.

To refine pricing, I’d analyze historical data to identify demand patterns, continuously updating the model to reflect real-world behaviors and preferences.”

14. 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.

You’ll need a commanding knowledge of diverse SQL functions, as Uber has several use cases when such functions are required. For example, the team may need to select manufacturers’ names in an unbiased manner.

How to Answer

Talk about the SQL function you’d use. This is also an excellent opportunity to show your understanding of data selection techniques that ensure fairness in the output.

Example

“I would write an SQL query that orders the list of manufacturers in the cars table by a random function, ensuring that each name has an equal chance of appearing at the top. Then, I would limit the output to just one row to get a single manufacturer’s name. This approach ensures that every time the query is run, a different manufacturer’s name could appear, each with an equal probability.”

15. You have been given a dataset containing the following information about Uber rides: ride ID, pickup datetime, dropoff datetime, pickup location, dropoff location, distance traveled, and fare. How would you estimate the average fare per kilometer for weekend rides vs weekdays?

This question assesses your ability to manipulate time-series data in Python.

How to Answer

Walk your interviewer through your approach step-by-step. Discuss calculating the average fare per kilometer by grouping the data accordingly and performing aggregate calculations.

Example

“I’d convert the pickup datetime into a format that allows identification of weekends and weekdays. This involves extracting the day of the week and applying a condition to classify each ride. I’d then calculate the fare per kilometer for each ride by dividing the total fare by the distance traveled. Finally, I’d group these calculations by the weekend/weekday classification to compute the average fare per kilometer for each group.”

16. Let’s say you’re 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?

Success metrics like driver lifetime value are essential in Uber’s business planning and strategy. This question tests your product sense and your ability to forecast long-term metrics.

How to Answer

Discuss how you would use historical data to calculate average earnings over a period, factor in driver churn rates, and apply a predictive model to estimate future earnings and engagement.

Example

“First, I’d analyze the data to calculate average earnings per driver and retention rates. Then, considering seasonal variations and growth in ride demand, I’d use these insights to project average monthly earnings. By combining these insights with historical churn rates, I’d model the expected active months for a new driver. Multiplying the projected monthly earnings by the expected active months would give us an estimate of the lifetime value of a new driver.”

17. 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?

Evaluating new product ideas is vital for diversifying Uber’s service offerings.

How to Answer

Discuss a multi-faceted approach involving market research and pilot testing. Emphasize understanding demand, operational implications, driver preferences, and regulatory considerations.

Example

“I’d start with market research to gauge customer interest in such an offering. This could involve surveys of current Uber users. I’d simultaneously analyze customer feedback and requests for pet accommodations in past rides to estimate demand. It’s also important to consult with drivers to hear their concerns. I’d lastly consider any regulatory restrictions related to transporting animals. Based on these insights, I’d recommend a pilot in select markets with high anticipated demand and pet ownership rates. This pilot would help us to collect real-world data on usage, customer satisfaction, and operational challenges, to help Uber decide whether and how to scale the ’Uber Pet’ service.”

18. 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?

This question assesses your understanding of predictive modeling in a real-world operational context.

How to Answer

You could suggest a regression model since the target variable is continuous. Discuss the selection of features that could influence preparation time.

Example

“I’d opt for a regression model since the output we’re predicting—time—is a continuous variable. Key features to include would be the number and type of items in the order, to account for complexity; time of day and day of the week, to reflect the restaurant’s busyness; historical preparation times for similar orders; and even the weather or special events, as these affect demand. Random forest or gradient boosting could be particularly effective due to their ability to handle non-linear relationships and interactions between features.”

19. What’s the relationship between PCA and K-means clustering?

This question tests your understanding of dimensionality reduction and clustering techniques and how they can be used together to enhance data analysis. It’s relevant for data science roles at Uber, where you’ll analyze complex datasets to optimize operations and targeting.

How to Answer

Discuss the conceptual link between PCA and K-means clustering, emphasizing PCA’s role in reducing dimensionality for more efficient and potentially more accurate clustering by K-means.

Example

“PCA and K-means clustering are often used together in data preprocessing. PCA reduces dimensionality by transforming data into a set of linearly uncorrelated components that retain most of the variations. This simplification can be helpful before applying K-means clustering, as it makes the clustering process more efficient. By focusing on the principal components, K-means has to deal with less noise and fewer irrelevant dimensions, which can lead to more meaningful clusters.”

20. We want to determine if Uber Eats has a net positive value for the company. How would you measure its success?

For Uber, understanding the value contributed by Uber Eats involves analyzing its direct financial performance and synergistic effects on the broader business ecosystem.

How to Answer

Outline a multidimensional approach that includes revenue analysis, market share and growth, customer acquisition and retention, and an evaluation of indirect benefits, such as brand enhancement. Stress the importance of comparing these outcomes against operational costs and investments.

Example

“To determine if Uber Eats has a net positive value for Uber, I’d start by looking at its direct financial contributions: revenues from delivery fees, commissions from restaurants, and any other income streams it has generated. I’d compare these revenues against the operational costs. Beyond these direct financial measures, it’s also important to assess market share and growth trends in the food delivery sector and the lifetime value of customers acquired through Uber Eats.

Also, evaluating the indirect benefits is crucial. For example, Uber Eats might enhance the Uber brand. It could also open up cross-marketing opportunities and provide valuable insights on consumer behavior.”

21. Write a function to get a sample from a standard normal distribution.

Understanding how to generate samples from a standard normal distribution is fundamental in many statistical and data science applications. This question assesses your knowledge of probability distributions and your ability to implement this in Python.

How to Answer

Explain the concept of a standard normal distribution and how it is defined by a mean of 0 and a standard deviation of 1. Then, describe how to use numpy’s np.random.normal function to generate a sample from this distribution.

Example

“To generate a sample from a standard normal distribution, I would use numpy’s np.random.normal function, which allows us to specify the mean and standard deviation of the distribution. Since the standard normal distribution has a mean of 0 and a standard deviation of 1, I would pass these values to the function. This function returns a random sample from a standard normal distribution, which is essential for simulations, hypothesis testing, and other statistical analyses.”

22. How would you build a model to predict which merchants the company should go after for acquisition when entering a new market?

Targeting the right merchants is critical when entering a new market, as it can significantly impact the success of the company’s expansion strategy. This question evaluates your ability to leverage data and predictive modeling to make strategic decisions.

How to Answer

Outline how you would use historical market data to identify key merchant characteristics associated with high performance. Explain how you would build a predictive model using these characteristics to score and prioritize potential merchants for acquisition.

Example

“First, I’d analyze historical data to identify merchant attributes correlated with high transaction volumes and customer retention, such as industry type, size, and location. Then, I’d use this data to train a predictive model that scores merchants based on their likelihood of success in the new market. Factors like local demand, competitive landscape, and past performance in similar markets would be incorporated into the model. Finally, I’d prioritize merchants with the highest scores for acquisition, ensuring that our resources are focused on the most promising opportunities.”

Here are some tips to help you excel in your interview.

Understand Uber’s Business Model and Challenges

Dive deep into Uber’s operations, including its revenue streams, cost structure, and key business metrics. Also, familiarize yourself with the challenges in the ride-sharing and food delivery sectors.

Explore the specific role at Uber through our Learning Paths to see how well your skills align with this position.

Visit Uber’s Careers page for tips on preparing for their interview.

Brush Up on Technical Skills

• Statistics and Probability: Be comfortable with concepts like hypothesis testing, A/B testing, confidence intervals, and Bayesian inference. Practice more statistics interview questions here.
• Machine Learning: Review supervised and unsupervised learning algorithms, focusing on use cases relevant to Uber, such as classification, regression, clustering, and time series forecasting.
• Data Manipulation and Analysis: Practice manipulating datasets using SQL and Python (particularly pandas and NumPy).

Review Past Projects and Case Studies

• Be prepared to discuss your past data science projects in detail, highlighting your problem-solving approach, the techniques you used, and the impact of your work.
• Look at case studies related to Uber’s problems, such as optimizing delivery routes for Uber Eats or predicting demand surges. Here is a detailed guide on solving a case study step-by-step by formulating metrics and utilizing SQL and Python.

Practice Problem-Solving and Behavioral Questions

Prepare for behavioral questions using the STAR method. Reflect on your past experiences and practice articulating them in a concise, impactful manner.

Visit our Interview Questions section to familiarize yourself with behavioral questions. It offers a wide range of practice questions to help structure your responses effectively using the STAR method.

To test your current preparedness for the interview process and improve your communication skills, try a mock interview.

Prepare Questions for the Interviewer

Prepare thoughtful questions for your interviewers about Uber’s work culture, challenges, and expectations. This shows your interest and eagerness to engage with the company’s ethos and future goals.

What is the average salary for a data scientist at Uber?

The average base salary for a data scientist at Uber is $125,986, making the remuneration competitive for prospective applicants.

For more insights into the salary range of data scientists at various companies, check out our comprehensive Data Scientist Salary Guide.

Where can I read more discussion posts on the Uber data scientist role on Interview Query?

Check out our discussion board, where Interview Query members talk about their experiences. You can use the search bar and filter for data science posts.

Are there job postings for Uber data science roles on Interview Query?

We list jobs for Uber. You can apply for them directly through our job portal. You can also filter by location, company, and position to see similar roles relevant to your career goals and skill set.

Succeeding in Uber data scientist interview questions requires solid technical skills and the ability to demonstrate your collaborative and critical thinking talents.

If you’re considering opportunities at other tech companies, check out our company interview guides. We cover a range of companies, including Google, IBM, and Apple.

For other data-related roles at Uber, consider exploring our guides for business analyst, data engineer, software engineer, and data analyst positions in our main Uber interview guide.

The key to your success is understanding Uber’s culture of innovation and collaboration and thoroughly preparing with both technical and behavioral questions.

Check out more of Interview Query’s content, and we hope you land your dream role at Uber soon!