Workday Data Scientist Interview Questions + Guide in 2025

Workday is a leading provider of enterprise cloud applications for finance and human resources, dedicated to delivering a brighter workday for all its employees and customers.

As a Data Scientist at Workday, you will be an integral part of the Workforce ML team, which aims to harness the power of AI and machine learning to drive innovative solutions for over 60 million users. Your key responsibilities will include developing, training, and testing machine learning models to address complex business problems, conducting statistical analysis to refine these models, and identifying new ML opportunities that can enhance customer experience. Strong programming skills in Python and expertise in machine learning libraries such as TensorFlow and PyTorch are essential, along with a solid foundation in data wrangling, preprocessing, and feature engineering.

You will collaborate closely with data scientists, ML engineers, product managers, and architects to define requirements and technical solutions, emphasizing a mindset of continuous improvement and a passion for quality and security. Excellent communication and teamwork skills are crucial, as you will be expected to engage in A/B testing, statistical analysis, and the active review of datasets to select the best methods for supervised learning.

This guide will help you prepare for your interview by providing insights into the role's expectations and the company culture, enabling you to present yourself confidently and effectively during your discussions with Workday.

The interview process for a Data Scientist role at Workday is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, each designed to evaluate different competencies.

1. Initial Phone Screen

The process typically begins with an initial phone screen conducted by a recruiter. This conversation lasts about 30 minutes and focuses on understanding the candidate's background, experiences, and motivations for applying to Workday. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role. This is an opportunity for candidates to express their interest and ask questions about the position.

2. Technical Phone Interview

Following the initial screen, candidates will participate in a technical phone interview, usually lasting around 45 minutes. This interview is conducted by a data scientist or technical lead and focuses on assessing the candidate's technical abilities. Expect questions related to machine learning algorithms, statistical analysis, and coding challenges. Candidates may be asked to solve problems in real-time, demonstrating their thought process and problem-solving skills.

3. Technical Assessment

In some cases, candidates may be required to complete a technical assessment or coding challenge. This assessment can involve tasks such as data analysis, model building, or coding exercises that require the application of data science principles. Candidates should be prepared to spend a significant amount of time on this task, as it may require thorough analysis and thoughtful solutions.

4. Onsite Interviews

The final stage of the interview process typically involves onsite interviews, which may be conducted virtually or in-person, depending on the circumstances. Candidates can expect to meet with multiple team members, including data scientists, machine learning engineers, and product managers. These interviews will cover a range of topics, including technical skills, project experiences, and behavioral questions. Each interview is designed to assess how well candidates can collaborate with cross-functional teams and contribute to the company's goals.

5. Final Discussion

After the onsite interviews, candidates may have a final discussion with a hiring manager or senior leader. This conversation often focuses on the candidate's fit within the team and the organization, as well as their long-term career aspirations. It’s also an opportunity for candidates to ask any remaining questions about the role or Workday's culture.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during this process.

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

Embrace the Conversational Style

Workday values a culture of openness and collaboration, which is reflected in their interview process. Expect a conversational tone rather than a rigid Q&A format. Prepare to share your experiences in a narrative style, focusing on "tell me about a time" scenarios. This approach allows you to showcase your problem-solving skills and how you work within a team. Be genuine and let your personality shine through, as they appreciate candidates who can connect on a personal level.

Prepare for Technical Challenges

As a Data Scientist, you will likely face technical assessments that require you to demonstrate your coding and analytical skills. Review key concepts in machine learning, data wrangling, and statistical analysis. Be ready to write code on the spot, as some interviewers may expect you to solve problems without relying on libraries. Practice common data science problems, including A/B testing, feature engineering, and model evaluation techniques. Familiarize yourself with the tools and technologies mentioned in the job description, such as Python, TensorFlow, and SQL.

Showcase Your Product Mindset

Workday is looking for candidates who can think critically about how machine learning can enhance customer experiences. Be prepared to discuss your thoughts on product development and how data-driven insights can inform business decisions. Share examples of how you've identified opportunities for improvement in past projects and how you proposed solutions. This will demonstrate your ability to align technical work with business objectives, a key aspect of the role.

Communicate Clearly and Effectively

Strong communication skills are essential for a Data Scientist at Workday. You will need to explain complex technical concepts to non-technical stakeholders. Practice articulating your thought process and findings in a clear and concise manner. Use visual aids or examples from your past work to illustrate your points. Being able to tell a compelling story with data will set you apart from other candidates.

Be Ready for Feedback and Adaptability

Candidates have noted that feedback during the interview process can be sparse. Approach the interview with a mindset of continuous improvement. If you receive constructive criticism, embrace it as an opportunity to learn and grow. Show your adaptability by discussing how you've adjusted your approach based on feedback in previous roles. This aligns with Workday's culture of valuing personal development and collaboration.

Understand the Company Culture

Workday prides itself on its employee-centric culture. Familiarize yourself with their core values and how they prioritize the well-being of their employees. During the interview, express your alignment with these values and how you can contribute to a positive work environment. Highlight experiences where you've fostered collaboration and inclusivity in your previous roles.

By following these tips, you will be well-prepared to navigate the interview process at Workday and demonstrate that you are a strong fit for the Data Scientist role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Workday. The interview process will likely focus on your technical skills, problem-solving abilities, and your understanding of machine learning and data analysis concepts. Be prepared to discuss your past experiences and how they relate to the role, as well as demonstrate your coding and analytical skills.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Discuss scenarios where you would use one over the other.

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 identify patterns or groupings, like clustering algorithms. For instance, I would use supervised learning for predicting sales based on historical data, while unsupervised learning could be applied to segment customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Discuss the project scope, your role, the challenges encountered, and how you overcame them. Highlight any specific techniques or tools you used.

Example

“I worked on a project to predict customer churn using logistic regression. One challenge was dealing with imbalanced data, which I addressed by implementing SMOTE for oversampling the minority class. This improved our model's accuracy significantly and provided actionable insights for the marketing team.”

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

This question tests your understanding of model evaluation metrics.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using multiple metrics depending on the problem. For classification tasks, I often look at accuracy, precision, and recall to understand the trade-offs between false positives and false negatives. For instance, in a medical diagnosis model, I prioritize recall to minimize missed positive cases, while in a spam detection model, precision is crucial to avoid false positives.”

4. What techniques do you use for feature selection?

This question gauges your knowledge of improving model performance through feature engineering.

How to Answer

Discuss techniques like recursive feature elimination, LASSO regression, or tree-based methods, and explain their importance.

Example

“I use techniques like recursive feature elimination and LASSO regression for feature selection. These methods help in identifying the most significant features that contribute to the model's predictive power, thus reducing overfitting and improving interpretability. For example, in a housing price prediction model, I used LASSO to eliminate irrelevant features, which enhanced the model's performance.”

Statistics & Probability

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

This question tests your understanding of statistical significance.

How to Answer

Define p-value and its role in hypothesis testing, and discuss its implications.

Example

“The p-value measures the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) indicates strong evidence against the null hypothesis, suggesting that we should reject it. For instance, in A/B testing, a p-value below 0.05 would lead me to conclude that the new feature significantly improves user engagement.”

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

This question assesses your foundational knowledge in statistics.

How to Answer

Explain the theorem and its implications for sampling distributions.

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 using sample statistics, which is fundamental in hypothesis testing and confidence interval estimation.”

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

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation or deletion, and when to use each.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. If the missing data is minimal, I might use mean or median imputation. However, if a significant portion is missing, I consider using predictive modeling techniques to estimate the missing values or even dropping the feature if it’s not critical. For instance, in a customer dataset, I used KNN imputation to fill in missing demographic information, which preserved the dataset's integrity.”

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

This question tests your understanding of statistical errors.

How to Answer

Define both types of errors and provide examples of their implications.

Example

“A Type I error occurs when we reject a true null hypothesis, also known as a false positive. Conversely, a Type II error happens when we fail to reject a false null hypothesis, or a false negative. For example, in a clinical trial, a Type I error could mean approving a drug that is ineffective, while a Type II error could mean rejecting a beneficial drug.”

Data Manipulation & Programming

1. How do you merge two datasets in Python?

This question assesses your programming skills and familiarity with data manipulation libraries.

How to Answer

Discuss the use of libraries like Pandas and the methods available for merging datasets.

Example

“I typically use the Pandas library to merge datasets. For instance, I would use the merge() function to combine two DataFrames based on a common key. If I have two datasets, one with customer information and another with transaction details, I would merge them on the customer ID to create a comprehensive view of customer behavior.”

2. Describe a time when you had to optimize a slow-running query.

This question evaluates your problem-solving and optimization skills.

How to Answer

Discuss the steps you took to identify the bottleneck and the optimizations you implemented.

Example

“I encountered a slow-running SQL query that was taking too long to execute due to multiple joins and a lack of indexing. I analyzed the query execution plan, identified the bottlenecks, and added indexes on the join columns. This reduced the query execution time from several minutes to under 30 seconds, significantly improving the reporting process.”

3. What is your experience with ETL processes?

This question assesses your knowledge of data pipelines and data integration.

How to Answer

Discuss your experience with ETL tools and the importance of ETL in data science.

Example

“I have experience designing ETL processes using tools like Apache Airflow and Talend. I understand the importance of extracting data from various sources, transforming it to meet business needs, and loading it into a data warehouse for analysis. For instance, I built an ETL pipeline that automated the data collection from multiple APIs, cleaned the data, and loaded it into a Snowflake database, which streamlined our reporting capabilities.”

4. Can you explain how you would implement a recommendation system?

This question tests your understanding of practical applications of machine learning.

How to Answer

Discuss the types of recommendation systems and the algorithms you would use.

Example

“I would implement a recommendation system using collaborative filtering or content-based filtering. For collaborative filtering, I would use user-item interaction data to identify patterns and suggest items based on similar users' preferences. For content-based filtering, I would analyze item features to recommend similar items to what the user has liked in the past. For example, in a movie recommendation system, I would use collaborative filtering to suggest movies based on users with similar viewing histories.”