What is a Data Scientist at Workiva?

The Data Scientist role at Workiva is a pivotal position that sits at the intersection of complex financial reporting, cloud platform scalability, and intelligent automation. Workiva is not just a SaaS provider; it is the backbone for thousands of organizations globally—including a vast majority of the Fortune 500—who rely on the Wdesk platform for connected reporting, ESG (Environmental, Social, and Governance) data management, and compliance.

In this role, you are not simply building models in a vacuum. You are expected to drive product innovation by leveraging data to solve intricate problems regarding document processing, anomaly detection in financial data, and user behavior analytics. You will work within cross-functional teams to embed intelligence into the platform, making compliance simpler and more accurate for users. The work you do directly impacts how major corporations communicate their most critical data to the world.

Candidates drawn to this position should be prepared for a culture that values innovation, transparency, and inclusivity. Workiva is renowned for its supportive environment, and the Data Scientist role reflects this: you will be encouraged to experiment, share knowledge, and solve problems collaboratively rather than competitively.

Getting Ready for Your Interviews

Preparing for an interview at Workiva requires a shift in mindset. While technical competence is non-negotiable, the company places an unusually high premium on thought process and cultural alignment. You should approach your preparation with the goal of demonstrating not just that you can solve a problem, but how you solve it and who you are as a teammate while doing so.

Your interviewers will focus on the following key evaluation criteria:

Applied Statistical & Technical Knowledge – 2–3 sentences describing: You must demonstrate a solid grasp of foundational concepts such as hypothesis testing, regression, and data manipulation using Python and SQL. Interviewers assess your ability to apply these concepts to real-world scenarios rather than just reciting textbook definitions. Expect to discuss the "why" behind your technical choices.

Structured Problem Solving – 2–3 sentences describing: Workiva values candidates who can navigate ambiguity. You will be evaluated on your ability to break down vague business problems into solvable data components. Interviewers are less interested in a single "correct" answer and more interested in seeing a logical, step-by-step approach to finding a solution.

Communication & Storytelling – 2–3 sentences describing: Data Scientists at Workiva must translate complex findings for non-technical stakeholders, including Product Managers and VPs. You will be tested on your ability to explain your methodology clearly and persuasively. Success here means making data accessible and actionable for the business.

Cultural Add & Inclusivity – 2–3 sentences describing: Workiva prides itself on a culture of belonging and low ego. Evaluators look for humility, a collaborative spirit, and a genuine respect for diverse perspectives. You should be ready to demonstrate how you support teammates and contribute to a positive working environment.

Interview Process Overview

The interview process for the Data Scientist role at Workiva is designed to be thorough yet respectful of your time and experience. Based on recent candidate data, the process is generally described as medium difficulty but highly positive in terms of experience. The timeline can vary, often taking approximately 4 weeks from application to final decision.

The flow typically begins with a recruiter phone screen to discuss your background and interest in the role. This is followed by a technical assessment or screen, which may involve a take-home task or a live coding session focusing on Python, SQL, and applied statistics. If successful, you will move to a series of video interviews, starting with the Hiring Manager and progressing to a virtual onsite loop. This final stage usually involves meeting with cross-functional partners, such as Directors or VPs, and deep-diving into both technical case studies and behavioral questions.

What makes Workiva’s process distinctive is the emphasis on psychological safety. Candidates frequently report that interviewers go out of their way to be friendly, inclusive (often sharing pronouns), and explicitly state that they do not want you to feel intimidated. The goal is to see your best work, not to trip you up with "gotcha" questions.

This timeline illustrates the typical progression from the initial touchpoint to the final offer. Use this to plan your preparation strategy: ensure your core technical skills are sharp for the early stages, and shift your focus toward behavioral storytelling and system design as you approach the final loop. Note that the "Technical Assessment" phase acts as a significant filter, so prioritize your coding and stats review early.

Deep Dive into Evaluation Areas

To succeed, you need to go beyond surface-level knowledge. Based on candidate reports, Workiva’s technical rounds are practical and discussion-heavy. You should be prepared to write code, but also to discuss the theoretical underpinnings of your models.

Applied Statistics and Probability

This is a core component of the Workiva technical assessment. You need to show that you understand the mathematical foundations of data science, not just how to import a library.

Be ready to go over:

• Hypothesis Testing – deeply understand p-values, null hypotheses, and how to select the correct test for a given dataset.
• Regression Analysis – understand the assumptions of linear/logistic regression and how to interpret coefficients.
• Experimental Design – how to set up A/B tests and determine sample sizes.
• Advanced concepts (less common) – Bayesian inference or time-series forecasting (depending on the specific team).

Example questions or scenarios:

• "How would you explain a p-value to a non-technical stakeholder?"
• "Describe a time you used hypothesis testing to solve a business problem."
• "What are the assumptions of linear regression, and how do you check for them?"

Coding and Data Manipulation

You will be expected to write clean, efficient code. The focus is usually on data manipulation and analysis rather than complex algorithmic puzzles (LeetCode style), though basic algorithmic competency is required.

Be ready to go over:

• Python (Pandas/NumPy) – efficient data cleaning, aggregation, and transformation.
• SQL – writing complex queries involving joins, window functions, and subqueries.
• Code Quality – writing readable, modular code that a team could maintain.

Example questions or scenarios:

• "Given a dataset of user logs, write a script to identify the top 10 most active users per region."
• "Write a SQL query to find the retention rate of customers over the last 12 months."
• "How would you handle missing data in a large dataset before feeding it into a model?"

Behavioral and Cultural Fit

Workiva places immense weight on this area. They are looking for people who embody their values of trust and collaboration.

Be ready to go over:

• Conflict Resolution – handling disagreements on technical approaches.
• Collaboration – working with engineers, product managers, and designers.
• Adaptability – handling changing requirements or ambiguous project scopes.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex technical concept to a senior leader."
• "Describe a situation where you disagreed with a teammate. How did you resolve it?"
• "How do you prioritize tasks when you have multiple conflicting deadlines?"

Key Responsibilities

As a Data Scientist at Workiva, your day-to-day work will revolve around leveraging data to enhance the Wdesk platform and internal decision-making. You will be responsible for the end-to-end data science lifecycle, from initial exploration to deployment and monitoring.

You will frequently collaborate with product management and engineering teams to identify opportunities where machine learning or statistical analysis can solve customer pain points. This might involve building models to automate data mapping for financial reports, detecting anomalies in user activity, or optimizing internal workflows. You are expected not just to build models, but to champion a data-driven culture within your immediate team.

Beyond modeling, a significant portion of your role involves communication and strategy. You will present your findings to stakeholders, translating raw data into strategic insights that drive product direction. You will also be responsible for maintaining the integrity of the data you use, ensuring that the solutions you build are robust, scalable, and compliant with the high standards required in the financial tech space.

Role Requirements & Qualifications

A strong candidate for this role combines solid technical foundations with the soft skills necessary to thrive in a collaborative SaaS environment.

• Technical Skills – Proficiency in Python or R is essential, with a strong preference for the Python data stack (Pandas, Scikit-learn). Advanced SQL skills are required for data extraction. Familiarity with cloud platforms (AWS, GCP) and version control (Git) is highly valued.
• Experience Level – Typically, Workiva looks for candidates with a Master’s degree or PhD in a quantitative field, or equivalent practical experience. For mid-level roles, 2+ years of industry experience in applying machine learning or statistics is standard.
• Soft Skills – Excellent verbal and written communication skills are critical. You must be able to articulate your thought process and work effectively in a remote or hybrid team structure.
• Nice-to-have vs. must-have –
  • Must-have: Strong grasp of statistics, Python/SQL proficiency, ability to explain technical concepts.
  • Nice-to-have: Experience with NLP (Natural Language Processing), knowledge of financial/accounting standards (XBRL), or experience with graph databases.

Common Interview Questions

The following questions are representative of what candidates have encountered at Workiva. While you should not memorize answers, you should use these to identify the patterns of inquiry. Notice the balance between technical execution and the "why" behind your decisions.

Technical & Statistics

These questions test your foundational knowledge and your ability to apply it.

• "What is the difference between Type I and Type II errors?"
• "How do you approach feature selection for a new model?"
• "Explain the concept of hypothesis testing and how you interpret results."
• "Write a SQL query to join two tables and filter for specific conditions (e.g., users who haven't logged in for 30 days)."
• "How would you validate a model before deploying it to production?"

Behavioral & Situational

These questions assess your fit within Workiva's collaborative culture.

• "Tell me about a time you made a mistake in your analysis. How did you handle it?"
• "Describe a project where you had to work with a difficult stakeholder."
• "How do you stay motivated when working on a long-term project with ambiguous goals?"
• "Tell me about a time you had to learn a new tool or technology quickly."

Problem Solving & Case Studies

These focus on your thought process and ability to structure ambiguity.

• "We want to measure the success of a new feature in Wdesk. what metrics would you track?"
• "How would you design an experiment to test if a new user interface improves engagement?"
• "If you noticed a sudden drop in a key metric, how would you investigate the cause?"

Frequently Asked Questions

Q: How technical is the interview process? The process is certainly technical, but it is distinct from "LeetCode-heavy" interviews at big tech firms. While you will write code (Python/SQL) and discuss statistics, the focus is heavily on your thought process and ability to apply concepts to real problems, rather than memorizing obscure algorithms.

Q: What is the culture like during the interview? Candidates consistently rate the Workiva interview experience as one of the best they have had. Interviewers are described as friendly, inclusive, and supportive. They often start by stating pronouns and explicitly aim to reduce your anxiety so you can perform your best.

Q: How long does the process take? Based on candidate data, the process typically takes about 4 weeks from the initial application to the final decision. However, HR is noted for being communicative and keeping candidates in the loop throughout the stages.

Q: Does Workiva offer remote roles? Yes, Workiva has a strong remote-work culture and offers flexible working arrangements. Many roles, including Data Science positions, are open to remote candidates, though there are also physical offices (e.g., Ames, IA; Denver, CO) for those who prefer hybrid work.

Q: Is domain knowledge in finance or accounting required? While Workiva operates in the financial reporting space, deep domain knowledge is usually considered a "nice-to-have" rather than a requirement. However, showing an interest in understanding the business context of the data is crucial.

Other General Tips

• Focus on the "Why": When solving a technical problem, articulate why you chose a specific statistical test or coding approach. Interviewers want to hear your justification, not just see the final output.
• Be Human: Workiva values authenticity. Don't be afraid to show your personality, admit what you don't know, and ask questions. The "no ego" policy is real.
• Review the STAR Method: For behavioral questions, structure your answers using the Situation, Task, Action, Result format. This ensures your stories are concise and impactful.
• Prepare for "Vague" Questions: You may be asked open-ended questions where there is no single right answer. In these cases, state your assumptions clearly and walk the interviewer through your logic step-by-step.
• Know the Product: Spend some time understanding what Wdesk does. Being able to link your data science skills to the specific challenges of financial reporting and compliance will set you apart.

Summary & Next Steps

The Data Scientist role at Workiva offers a unique opportunity to apply sophisticated statistical and machine learning techniques to mission-critical financial data. It is a role where technical skill meets strategic impact, all within a company culture that is widely recognized for its inclusivity and support. If you are a curious problem-solver who enjoys collaboration and wants to see your work influence major global enterprises, this is a role worth pursuing.

To succeed, focus your preparation on applied statistics, practical Python/SQL skills, and behavioral storytelling. Remember that Workiva is looking for a colleague, not just a calculator. Approach your interviews with confidence, be ready to explain your thought process, and lean into the collaborative nature of the discussions.

This salary module provides a baseline for compensation expectations. Keep in mind that Workiva offers a competitive total rewards package that often includes equity (RSUs) and performance bonuses, which can significantly increase the total compensation beyond the base salary shown. Be prepared to discuss your expectations transparently with the recruiter early in the process.

You have the skills to excel in this process. Trust your preparation, engage with your interviewers as future teammates, and demonstrate the thoughtful, data-driven approach that defines a successful Workiva Data Scientist. Good luck!