Sonder Inc. Data Scientist Interview Questions + Guide in 2025

Sonder Inc. is dedicated to transforming mental healthcare through innovative technology, providing accessible and personalized services that foster high-quality outcomes for patients.

As a Data Scientist at Sonder Inc., you will play a pivotal role in leveraging data to enhance mental healthcare delivery. Your key responsibilities will include deploying machine learning models and services to improve user experiences for both clinicians and clients. You will lead the design and architecture of algorithms for new initiatives, prototype ML services, and ensure best practices through code reviews. Your analytical skills will be essential as you examine existing data to identify improvement opportunities and collaborate with engineering teams to integrate ML functionalities into the platform. A strong foundation in statistics, proficiency in SQL, and experience in developing data-driven solutions will be critical, as will your ability to communicate effectively with cross-functional teams.

Success in this role will be measured by your ability to deploy impactful data products, demonstrate leadership in problem-solving, and foster positive interactions within the organization. This guide will help you prepare for the interview by outlining the skills and experiences that are essential for the role, as well as the challenges you may face during the process.

The interview process for a Data Scientist role at Sonder Inc. is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds as follows:

1. Application and Initial Contact

After submitting your application, you can expect a prompt response from the HR team, usually within a few days. This initial contact will involve scheduling a phone interview to discuss your background, the role, and your interest in Sonder Inc. It’s important to be prepared to articulate your experience and how it aligns with the company’s mission in mental healthcare.

2. HR Screening Interview

The HR screening interview is generally a 30- to 60-minute call where the recruiter will delve into your resume, clarify your experiences, and gauge your fit for the company culture. This conversation may also cover your availability for subsequent interviews and provide an overview of the next steps in the process.

3. Technical Interview with Hiring Manager

Following the HR screening, candidates typically participate in a technical interview with the hiring manager. This interview focuses on your technical expertise, particularly in SQL, statistics, and algorithms. Expect to discuss your previous projects, methodologies, and how you approach problem-solving in data science. Be prepared for a mix of technical questions and discussions about your experience with machine learning and data product deployment.

4. Take-Home Assignment

Candidates may be required to complete a take-home assignment designed to evaluate your practical skills in data science. This assignment usually has a time limit, but it’s advisable to allocate additional time to ensure thoroughness and quality in your submission. The assignment may involve data analysis, model building, or other relevant tasks that reflect the work you would be doing at Sonder Inc.

5. Final Interview Round

The final round often consists of one or more interviews with team members or stakeholders. This stage may include behavioral questions to assess your teamwork, communication skills, and alignment with the company’s values. You may also be asked to present your take-home assignment and discuss your thought process and findings.

Throughout the interview process, it’s crucial to demonstrate not only your technical capabilities but also your passion for improving mental healthcare through data-driven solutions.

Now, let’s explore the specific interview questions that candidates have encountered during this process.

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

Understand the Company Culture

Sonder Inc. has received mixed reviews regarding its management and employee treatment. It's crucial to approach your interview with a clear understanding of the company culture. Be prepared to discuss how your values align with Sonder's mission of providing accessible mental healthcare. Show that you are not only technically proficient but also genuinely interested in contributing to a positive work environment and improving the experiences of both clinicians and clients.

Prepare for the Take-Home Assignment

Candidates have noted that the take-home assignment can take longer than the suggested time limit if you want to produce quality work. Treat this assignment as an opportunity to showcase your skills in SQL and statistics. Make sure to allocate sufficient time to thoroughly understand the problem, design your solution, and document your thought process clearly. This will demonstrate your analytical abilities and attention to detail, which are critical for a Data Scientist role.

Be Ready for Technical Discussions

During the interview, you may encounter a mix of technical and HR questions. Given the emphasis on SQL and statistics in the role, be prepared to discuss your experience with data manipulation, statistical analysis, and any relevant projects. Highlight your proficiency in Python and your ability to deploy machine learning models. Be ready to explain your thought process and the methodologies you used in past projects, as this will help you stand out.

Showcase Your Problem-Solving Skills

Sonder is looking for candidates who can independently identify opportunities for data science applications. Prepare examples from your past experience where you successfully solved complex problems or improved processes using data-driven insights. This will not only demonstrate your technical skills but also your proactive approach to challenges.

Engage with the Interviewers

Candidates have reported that some interviews felt one-sided, with interviewers dominating the conversation. Make a conscious effort to engage with your interviewers by asking insightful questions about the team, projects, and company direction. This will show your enthusiasm for the role and help you gauge if the company is the right fit for you.

Emphasize Collaboration and Communication

Given the cross-functional nature of the role, it's essential to highlight your ability to work collaboratively with engineering and product teams. Discuss any experiences where you successfully communicated complex technical concepts to non-technical stakeholders. This will demonstrate your ability to bridge the gap between data science and practical application, which is vital for success at Sonder.

Follow Up Thoughtfully

After your interview, consider sending a follow-up email thanking your interviewers for their time. Use this opportunity to reiterate your interest in the role and briefly mention any key points from the interview that resonated with you. This not only shows professionalism but also reinforces your enthusiasm for the position.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically skilled but also a good cultural fit for Sonder Inc. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Sonder Inc. The interview process will likely focus on your technical skills, particularly in SQL, statistics, and algorithms, as well as your ability to apply these skills in the context of mental healthcare. Be prepared to discuss your experience with data-driven decision-making, model deployment, and collaboration with cross-functional teams.

SQL

1. How would you optimize a slow SQL query?

Understanding how to improve query performance is crucial for data scientists who work with large datasets.

How to Answer

Discuss specific techniques such as indexing, query restructuring, and analyzing execution plans. Mention any tools you’ve used to identify bottlenecks.

Example

“I would start by analyzing the execution plan to identify any slow operations. Then, I would consider adding indexes on frequently queried columns and rewriting the query to minimize the number of joins or subqueries, which can significantly improve performance.”

2. Can you explain the difference between INNER JOIN and LEFT JOIN?

This question tests your understanding of SQL joins, which are fundamental for data manipulation.

How to Answer

Clearly define both types of joins and provide examples of when you would use each.

Example

“An INNER JOIN returns only the rows that have matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. I would use a LEFT JOIN when I want to include all records from the left table, even if there are no matches in the right table.”

3. How do you handle NULL values in SQL?

Handling NULL values is essential for accurate data analysis.

How to Answer

Discuss methods such as using COALESCE, ISNULL, or filtering out NULLs in your queries.

Example

“I typically use the COALESCE function to replace NULL values with a default value, ensuring that my calculations remain accurate. For instance, in a sales dataset, I might replace NULL sales figures with zero to avoid skewing the total revenue calculations.”

4. What are window functions in SQL, and when would you use them?

Window functions are powerful tools for performing calculations across a set of table rows related to the current row.

How to Answer

Explain what window functions are and provide a scenario where they would be beneficial.

Example

“Window functions allow you to perform calculations across a set of rows without collapsing the result set. For example, I would use a window function to calculate a running total of sales over time while still displaying each individual sale record.”

Statistics

1. Explain the concept of p-value in hypothesis testing.

Understanding p-values is critical for making data-driven decisions.

How to Answer

Define p-value and its significance in hypothesis testing, and provide context for its interpretation.

Example

“A p-value indicates the probability of observing the data, or something more extreme, given that the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

2. What is the difference between Type I and Type II errors?

This question assesses your understanding of statistical errors.

How to Answer

Clearly define both types of errors and provide examples of each.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a clinical trial, a Type I error could mean concluding a treatment is effective when it is not, while a Type II error could mean missing the opportunity to identify an effective treatment.”

3. How would you design an A/B test?

A/B testing is a common method for evaluating the effectiveness of changes in a product or service.

How to Answer

Discuss the steps involved in designing an A/B test, including sample size determination, control and treatment groups, and metrics for success.

Example

“I would start by defining a clear hypothesis and determining the key metrics to measure success. Then, I would calculate the required sample size to ensure statistical significance, randomly assign users to control and treatment groups, and analyze the results using appropriate statistical tests to draw conclusions.”

4. Can you explain the Central Limit Theorem?

This theorem is fundamental in statistics and has implications for sampling distributions.

How to Answer

Define the Central Limit Theorem and explain its importance in statistical analysis.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial because it allows us to make inferences about population parameters even when the population distribution is unknown.”

Algorithms

1. Describe a machine learning algorithm you have implemented.

This question assesses your practical experience with algorithms.

How to Answer

Choose an algorithm you are familiar with, explain its purpose, and describe how you implemented it.

Example

“I implemented a decision tree algorithm for a customer segmentation project. I used Python’s Scikit-learn library to train the model on historical customer data, optimizing the parameters through cross-validation to improve accuracy.”

2. How do you evaluate the performance of a machine learning model?

Understanding model evaluation is key to ensuring the effectiveness of your solutions.

How to Answer

Discuss various metrics you use to evaluate model performance, such as accuracy, precision, recall, and F1 score.

Example

“I evaluate model performance using a combination of metrics. For classification tasks, I look at accuracy, precision, and recall to understand the model's strengths and weaknesses. I also use the F1 score to balance precision and recall, especially in cases where class distribution is imbalanced.”

3. What is overfitting, and how can you prevent it?

Overfitting is a common issue in machine learning that can lead to poor model performance.

How to Answer

Define overfitting and discuss techniques to prevent it, such as regularization and cross-validation.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, resulting in poor generalization to new data. To prevent overfitting, I use techniques like regularization, pruning decision trees, and employing cross-validation to ensure the model performs well on unseen data.”

4. Can you explain the concept of feature engineering?

Feature engineering is crucial for improving model performance.

How to Answer

Discuss the importance of feature engineering and provide examples of techniques you have used.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. For instance, in a time series analysis, I might create features like moving averages or lagged values to capture trends and seasonality, which can significantly enhance the predictive power of the model.”