Smartsheet Data Scientist Interview Questions + Guide in 2025

Smartsheet is a tech company that empowers teams to manage projects and automate workflows using innovative no-code tools.

As a Data Scientist at Smartsheet, you will play a critical role in driving data-driven insights that influence strategic decisions across the organization. You will leverage your expertise in data analytics and machine learning to inform product development, optimize user experiences, and enhance key performance metrics such as user acquisition, engagement, retention, and growth. This role requires a strong analytical mindset, proficiency in programming languages like Python or R, and expertise in SQL and visualization tools such as Tableau. You will collaborate closely with cross-functional teams, including Product, Marketing, and Engineering, to understand their objectives and deliver impactful insights.

The ideal candidate is someone who thrives in a dynamic environment, possesses excellent communication skills to present complex analyses clearly, and is capable of driving a data culture within the teams they work with. Your experience in product analytics, statistical techniques, and experimental design will be essential in conducting experiments and A/B tests to measure the impact of product changes.

This guide will help you prepare for your interview by giving you insights into the expectations and requirements of the role, as well as tips on how to effectively communicate your experience and expertise.

The interview process for a Data Scientist role at Smartsheet is structured to assess both technical and cultural fit, ensuring candidates align with the company's values and expectations. The process typically unfolds in several stages, allowing candidates to showcase their skills and experiences comprehensively.

1. Initial Screening

The first step involves a 30-minute phone call with a recruiter. This conversation is primarily focused on understanding the candidate's background, motivations, and fit for the company culture. The recruiter will also provide insights into the role and the expectations from the Data Science team.

2. Technical Interview

Following the initial screening, candidates will participate in a technical interview, which usually lasts about 45 minutes. This session may include coding challenges, SQL syntax questions, and discussions around machine learning concepts. Candidates should be prepared to demonstrate their analytical skills and problem-solving abilities, often through practical coding exercises or algorithmic questions.

3. Interview Loop

The final stage consists of a loop interview, which can last several hours and typically involves multiple interviewers. This stage is designed to evaluate both technical and behavioral competencies. Candidates can expect to engage in a series of one-on-one interviews that cover a range of topics, including:

• Technical Skills: Candidates will face questions related to data analytics, machine learning techniques, and statistical methods. They may be asked to solve problems on a whiteboard or through a coding platform, focusing on their approach to data-driven decision-making.

• Behavioral Questions: Interviewers will assess how candidates have handled past challenges, their teamwork experiences, and their ability to communicate complex analyses effectively. Questions may revolve around specific projects, collaboration with cross-functional teams, and how they drive data culture within organizations.

• Cultural Fit: Smartsheet places a strong emphasis on cultural alignment, so candidates should be ready to discuss their values and how they resonate with the company's mission and work environment.

After completing the interview loop, candidates may experience a delay in feedback, as the company is known for its slower communication process post-interview.

As you prepare for your interview, consider the types of questions that may arise in these stages, focusing on your technical expertise and past experiences.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Smartsheet. The interview process will likely focus on your technical skills in data science, machine learning, and statistics, as well as your ability to communicate insights effectively and work collaboratively with cross-functional teams. Be prepared to discuss your past experiences and how they relate to the role.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the distinction between these two types of learning is fundamental in data science.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of algorithms used in each. Highlight the scenarios in which each type is applicable.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using regression or classification algorithms. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, like clustering algorithms.”

2. Describe a machine learning project you worked on. What was your role?

This question assesses your practical experience with machine learning.

How to Answer

Outline the project’s objectives, your specific contributions, and the outcomes. Emphasize any challenges you faced and how you overcame them.

Example

“I worked on a customer segmentation project where I developed a clustering model to identify distinct user groups. My role involved data preprocessing, feature selection, and implementing the K-means algorithm. The insights helped the marketing team tailor their campaigns, resulting in a 20% increase in engagement.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and improvement techniques.

How to Answer

Discuss various strategies to prevent overfitting, such as cross-validation, regularization, and pruning techniques.

Example

“To combat overfitting, I use techniques like cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization methods like L1 or L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model evaluation.

How to Answer

Mention various metrics relevant to the type of model (e.g., accuracy, precision, recall, F1 score for classification; RMSE, MAE for regression) and explain when to use each.

Example

“I typically use accuracy and F1 score for classification models to balance precision and recall. For regression models, I prefer RMSE as it provides a clear measure of prediction error, allowing me to understand the model's performance better.”

Statistics & Probability

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

This question assesses your understanding of statistical significance.

How to Answer

Define p-value and its role in hypothesis testing, including what it indicates about the null hypothesis.

Example

“A p-value represents the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that the observed effect is statistically significant.”

2. What is the Central Limit Theorem and why is it important?

This question tests your grasp of fundamental statistical concepts.

How to Answer

Explain the theorem and its implications for sampling distributions and inferential statistics.

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 for making inferences about population parameters based on sample statistics.”

3. How do you determine if a dataset is normally distributed?

This question evaluates your ability to analyze data distributions.

How to Answer

Discuss methods such as visual inspection (histograms, Q-Q plots) and statistical tests (Shapiro-Wilk, Kolmogorov-Smirnov).

Example

“I assess normality by creating a histogram and a Q-Q plot to visually inspect the distribution. Additionally, I perform the Shapiro-Wilk test, where a p-value greater than 0.05 indicates that the data does not significantly deviate from normality.”

4. Can you explain the difference between Type I and Type II errors?

This question assesses your understanding of error types in hypothesis testing.

How to Answer

Define both types of errors and provide examples to illustrate the differences.

Example

“A Type I error occurs when we incorrectly reject a true null hypothesis, often referred to as a false positive. Conversely, a Type II error happens when we fail to reject a false null hypothesis, known as a false negative. Understanding these errors is vital for interpreting the results of hypothesis tests.”

SQL and Data Manipulation

1. What is the difference between INNER JOIN and LEFT JOIN in SQL?

This question tests your SQL knowledge and ability to manipulate data.

How to Answer

Explain the differences in how each join operates and provide examples of when to 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, filling in NULLs for non-matching rows. I use INNER JOIN when I need only the intersecting data, and LEFT JOIN when I want to retain all records from the left table.”

2. How would you optimize a slow-running SQL query?

This question evaluates your problem-solving skills in database management.

How to Answer

Discuss techniques such as indexing, query rewriting, and analyzing execution plans.

Example

“To optimize a slow-running query, I would first analyze the execution plan to identify bottlenecks. Then, I might add indexes to frequently queried columns, rewrite the query to reduce complexity, or break it into smaller, more manageable parts to improve performance.”

3. Can you explain the concept of normalization in databases?

This question assesses your understanding of database design principles.

How to Answer

Define normalization and its purpose, mentioning the different normal forms.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related tables and defining relationships between them. The first three normal forms are commonly used to achieve this.”

4. What is a window function in SQL, and how is it used?

This question tests your advanced SQL knowledge.

How to Answer

Explain what window functions are and provide examples of their applications.

Example

“A window function performs a calculation across a set of table rows that are related to the current row. For instance, I can use the ROW_NUMBER() function to assign a unique sequential integer to rows within a partition of a result set, which is useful for ranking data without collapsing the result set.”