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.

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.

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?"

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.