Workiva Data Scientist Interview Questions + Guide in 2025

Workiva is a leading provider of cloud-based solutions that streamline and enhance the reporting and compliance processes for businesses across various industries.

The Data Scientist role at Workiva is integral to transforming complex data into actionable insights that drive product features and decision-making. Key responsibilities include engaging in exploratory data analysis, collaborating with data engineers and machine learning engineers to optimize data pipelines, and prototyping machine learning models to solve real-world problems. Ideal candidates will possess a strong foundation in statistics and probability, with a particular emphasis on data cleaning and analysis techniques. Proficiency in Python and familiarity with machine learning algorithms are essential, as is the ability to work collaboratively within a diverse team environment. A passion for innovation and a commitment to the company's values—customer success, trust, integrity, collaboration, innovation, inclusion, and accountability—are critical traits for success in this role.

This guide will help you prepare effectively for your interview by providing insights into the skills and experiences that Workiva values, along with an understanding of the company's culture and expectations.

The interview process for a Data Scientist role at Workiva is structured to assess both technical skills and cultural fit, ensuring candidates align with the company's values and collaborative environment. The process typically unfolds in several stages:

1. Initial Phone Screening

The first step is a phone screening conducted by a recruiter. This conversation usually lasts around 30 minutes and focuses on your background, experiences, and motivations for applying to Workiva. The recruiter will also gauge your fit for the company culture and provide insights into the role and expectations.

2. Technical Interview

Following the initial screening, candidates are invited to a technical interview, which may be conducted via video conferencing. This stage often includes coding exercises, where you may be asked to solve problems using programming languages relevant to the role, such as Python or JavaScript. Expect to demonstrate your understanding of statistical concepts, algorithms, and machine learning techniques, as well as your ability to work with data.

3. Behavioral Interview

After the technical assessment, candidates typically participate in a behavioral interview. This round is designed to evaluate how you handle various workplace scenarios and conflicts. Interviewers may ask you to share experiences that highlight your problem-solving skills, teamwork, and adaptability. Questions may revolve around past projects, challenges faced, and how you contributed to team success.

4. Panel Interview

In some cases, candidates may be invited to a panel interview, where they will meet with multiple team members, including data scientists and managers. This stage allows interviewers to assess your fit within the team and your ability to communicate complex ideas effectively. You may be asked to present a case study or discuss a project you have worked on, showcasing your analytical skills and thought process.

5. Final Assessment

The final stage may involve a more in-depth assessment, such as a take-home project or a presentation. This step is designed to evaluate your technical skills in a practical context, allowing you to demonstrate your ability to analyze data, build models, and derive insights that align with Workiva's objectives.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, focusing on your technical expertise and experiences that reflect your alignment with Workiva's values.

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

Understand the Company Culture

Workiva places a strong emphasis on collaboration, trust, and integrity. Familiarize yourself with their core values and be prepared to discuss how your personal values align with theirs. During the interview, demonstrate your understanding of their culture by sharing examples of how you have embodied these values in your previous experiences. This will show that you are not only a fit for the role but also for the company as a whole.

Prepare for Technical Assessments

Given the technical nature of the Data Scientist role, you should be well-versed in statistics, algorithms, and programming languages like Python. Brush up on your knowledge of statistical concepts, including distributions and hypothesis testing, as well as machine learning techniques such as classification and regression. Be ready to tackle coding challenges, possibly involving data manipulation or algorithm implementation, as these are common in the interview process.

Emphasize Problem-Solving Skills

Workiva values candidates who can approach open-ended problems with a data-driven mindset. Be prepared to discuss past experiences where you successfully solved complex problems using data analysis. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on your thought process and the impact of your solutions.

Be Ready for Behavioral Questions

Expect a mix of technical and behavioral questions during your interviews. Prepare to discuss scenarios where you faced challenges, worked in teams, or had to make difficult decisions. Questions like "Tell me about a time you disagreed with your team" or "Describe a conflict you once had" are common. Reflect on your past experiences and be ready to articulate how you handled these situations, emphasizing your communication and collaboration skills.

Showcase Your Passion for Data Science

Demonstrate your enthusiasm for the field of data science and your eagerness to learn and grow. Discuss any relevant projects or coursework that showcase your skills and interest in data analysis, machine learning, or statistical modeling. If you have experience with data science libraries like pandas or scikit-learn, be sure to mention it, as this will highlight your hands-on experience.

Follow Up and Stay Engaged

After your interviews, don’t hesitate to follow up with your recruiter or interviewers. Express your gratitude for the opportunity and reiterate your interest in the role. This not only shows your enthusiasm but also helps you stay on their radar during the decision-making process. Remember, communication is key, and being proactive can set you apart from other candidates.

By preparing thoroughly and aligning your experiences with Workiva's values and expectations, you can confidently approach your interview and increase your chances of success. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Workiva. The interview process will likely assess your technical skills in statistics, machine learning, and programming, as well as your ability to work collaboratively and communicate effectively. Be prepared to discuss your past experiences and how they relate to the responsibilities of the role.

Statistics and Probability

1. Can you explain the difference between a population and a sample?

Understanding the distinction between these two concepts is fundamental in statistics and will help you demonstrate your grasp of statistical principles.

How to Answer

Discuss the definitions of population and sample, emphasizing the importance of sampling in statistical analysis.

Example

“A population includes all members of a defined group, while a sample is a subset of that population. Sampling is crucial because it allows us to make inferences about the population without needing to collect data from every individual, which can be impractical.”

2. What is a p-value, and how do you interpret it?

This question tests your knowledge of hypothesis testing and statistical significance.

How to Answer

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

Example

“A p-value is the probability of observing results as extreme as the ones obtained, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

3. Describe a time you used statistical methods to solve a problem.

This question allows you to showcase your practical experience with statistics.

How to Answer

Provide a specific example where you applied statistical methods to derive insights or solve a problem, detailing the methods used and the outcome.

Example

“In a previous project, I used regression analysis to identify factors affecting customer churn. By analyzing historical data, I was able to pinpoint key variables and recommend strategies that reduced churn by 15%.”

4. How do you handle missing data in a dataset?

This question assesses your data cleaning and preprocessing skills.

How to Answer

Discuss various techniques for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I typically assess the extent of missing data first. If it’s minimal, I might use mean imputation. For larger gaps, I prefer to use predictive modeling techniques to estimate missing values, ensuring that the integrity of the dataset is maintained.”

Machine Learning

1. What is the difference between supervised and unsupervised learning?

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Define both types of learning and provide examples of algorithms used in each.

Example

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

2. Can you explain the concept of overfitting and how to prevent it?

This question evaluates your understanding of model performance and generalization.

How to Answer

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

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. To prevent this, I use techniques like cross-validation to ensure the model generalizes well and apply regularization methods to penalize overly complex models.”

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

This question allows you to highlight your hands-on experience with machine learning.

How to Answer

Detail a specific project, your contributions, the challenges faced, and the results achieved.

Example

“I worked on a project to predict sales using time series analysis. My role involved data preprocessing, feature engineering, and model selection. I implemented an ARIMA model, which improved our sales forecast accuracy by 20%.”

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

This question assesses your knowledge of model evaluation techniques.

How to Answer

Discuss various metrics relevant to different types of models, such as accuracy, precision, recall, and F1 score.

Example

“I use different metrics based on the problem type. For classification tasks, I focus on accuracy, precision, and recall, while for regression, I look at RMSE and R-squared to evaluate model performance.”

Programming and Algorithms

1. What programming languages are you proficient in, and how have you used them in data science?

This question gauges your technical skills and experience with programming.

How to Answer

List the languages you are proficient in and provide examples of how you have applied them in data science projects.

Example

“I am proficient in Python and R. I primarily use Python for data manipulation with libraries like pandas and NumPy, and for machine learning with scikit-learn. In a recent project, I used Python to automate data cleaning processes, which saved the team significant time.”

2. Can you explain a data structure you frequently use and why?

This question tests your understanding of data structures and their applications.

How to Answer

Choose a data structure, explain its characteristics, and discuss its use cases in data science.

Example

“I frequently use dictionaries in Python because they allow for fast lookups and are ideal for storing key-value pairs. For instance, I used a dictionary to map user IDs to their respective purchase histories, enabling quick access during analysis.”

3. Describe a challenging algorithmic problem you solved.

This question allows you to demonstrate your problem-solving skills.

How to Answer

Provide a specific example of an algorithmic challenge, the approach you took, and the outcome.

Example

“I faced a challenge in optimizing a sorting algorithm for a large dataset. I implemented a quicksort algorithm, which significantly reduced the processing time from hours to minutes, improving overall efficiency.”

4. How do you ensure the quality of your code?

This question assesses your coding practices and attention to detail.

How to Answer

Discuss practices such as code reviews, unit testing, and documentation that you use to maintain code quality.

Example

“I ensure code quality by conducting regular code reviews with my peers, writing unit tests to validate functionality, and maintaining thorough documentation. This approach not only improves code reliability but also facilitates collaboration within the team.”