Degreed Data Scientist Interview Questions + Guide in 2025

Degreed is a leading platform that helps individuals and organizations discover, curate, and share learning content to enhance skills and drive personal and professional growth.

As a Data Scientist at Degreed, you will play a pivotal role in leveraging data to inform business decisions and improve user experiences. Key responsibilities include conducting in-depth analyses of user behavior and learning patterns, developing predictive models, and collaborating with cross-functional teams to translate complex data into actionable insights. You will be expected to possess strong skills in statistical analysis, machine learning, and data visualization tools, with a particular emphasis on Python, Pandas, and modeling techniques. An ideal candidate will demonstrate excellent problem-solving abilities, effective communication skills, and a deep understanding of business metrics to drive strategic initiatives.

This guide will equip you with the insights and knowledge necessary to navigate the interview process confidently, showcasing your expertise and alignment with Degreed's mission and values.

The interview process for a Data Scientist role at Degreed is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. Initial Screening

The initial screening is a brief phone interview, usually lasting around 30 minutes, conducted by a recruiter. This conversation serves to gauge your interest in the role and the company, as well as to discuss your background, skills, and career aspirations. The recruiter will also evaluate your alignment with Degreed's values and culture, which is crucial for success in the organization.

2. Take-Home Challenge

Following the initial screening, candidates are often required to complete a take-home challenge. This task is designed to be open-ended, allowing you to demonstrate your problem-solving abilities and analytical thinking. You will need to identify a problem, formulate a solution, and apply your data science skills to communicate your findings effectively. Emphasis is placed on the clarity of your thought process, the quality of your documentation, and the presentation of your results.

3. Technical Interview

The technical interview typically involves a video call with a data scientist or a panel of interviewers. During this session, you will be asked to discuss your previous projects in detail, focusing on your role, the challenges faced, and how you collaborated with stakeholders. Expect to delve into topics such as probability, product metrics, analytical techniques, and machine learning knowledge. You may also be asked to solve coding problems, so proficiency in Python and libraries like Pandas is essential.

4. Onsite Interviews

The final stage usually consists of onsite interviews, which may include multiple rounds with different team members. Each round will cover various aspects of data science, including modeling, machine learning implementation, and system design. Behavioral questions will also be integrated to assess how you work within a team and handle challenges. Each interview is designed to evaluate both your technical expertise and your ability to communicate complex ideas clearly and effectively.

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

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

Prepare to Discuss Your Projects in Detail

Be ready to dive deep into a previous project where you played a significant role. Highlight the challenges you faced, how you collaborated with stakeholders, and the methods you used to communicate your findings and solutions. This is your opportunity to showcase not just your technical skills, but also your ability to work within a team and convey complex information clearly.

Master the Take-Home Challenge

Expect a take-home challenge that may be open-ended and lacks a definitive solution. Focus on clearly identifying the problem, formulating a structured approach to your solution, and documenting your thought process. Make sure to present your findings in a way that is easy to understand, as clarity in communication is crucial.

Emphasize Code Quality and Readability

When working on coding tasks, prioritize code readability and maintainability. Use clear variable names, write comments where necessary, and structure your code logically. This not only demonstrates your technical proficiency but also shows that you value collaboration and future code maintenance.

Showcase Your Analytical Skills

Be prepared to discuss your knowledge of probability, product metrics, and analytical techniques. Familiarize yourself with key concepts in modeling and machine learning, as well as their practical applications. This will help you articulate your analytical thought process during the interview.

Communicate Effectively

Throughout the interview, focus on clear and concise communication. Whether you are discussing your past experiences or presenting your solutions, aim to convey your ideas in a straightforward manner. This aligns with Degreed's emphasis on effective communication and collaboration.

Understand the Company Culture

Degreed values innovation, collaboration, and a growth mindset. Familiarize yourself with their mission and how they approach learning and development. Tailor your responses to reflect these values, demonstrating that you are not only a fit for the role but also for the company culture.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Scientist role at Degreed. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Degreed. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data-driven decision-making. Be prepared to discuss your past projects in detail, as well as your approach to modeling, machine learning, and data analysis.

Experience and Background

1. Can you describe a data science project you led and the challenges you faced?

Degreed values hands-on experience, and they will want to understand how you navigate obstacles in your projects.

How to Answer

Discuss the project’s objectives, the specific challenges you encountered, and how you overcame them. Highlight your role in the project and the impact of your contributions.

Example

“I led a project aimed at predicting user engagement on our platform. One major challenge was dealing with incomplete data. I collaborated with the data engineering team to fill in gaps and used imputation techniques to ensure our model was robust. This ultimately improved our prediction accuracy by 15%.”

Machine Learning

2. What machine learning algorithms are you most comfortable with, and why?

Understanding your familiarity with various algorithms is crucial for assessing your technical fit.

How to Answer

Mention specific algorithms you have used, the contexts in which you applied them, and why you prefer them for certain tasks.

Example

“I am most comfortable with decision trees and random forests due to their interpretability and effectiveness in handling non-linear relationships. For instance, I used a random forest model to classify user behavior, which provided clear insights into feature importance.”

3. How do you approach feature selection in your models?

Feature selection is critical for model performance, and they will want to know your methodology.

How to Answer

Explain your process for selecting features, including any techniques or tools you use to evaluate their importance.

Example

“I typically use a combination of domain knowledge and statistical methods like recursive feature elimination. For a recent project, I started with a large feature set and iteratively removed the least significant features based on their p-values, which improved our model’s performance.”

Statistics & Probability

4. Explain the concept of p-values and their significance in hypothesis testing.

A solid understanding of statistics is essential for a data scientist, and they will test your knowledge in this area.

How to Answer

Define p-values and explain their role in determining statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, which is crucial for validating our findings in A/B testing scenarios.”

Data Analysis & Metrics

5. How do you measure the success of a data science project?

Degreed will want to know how you evaluate the impact of your work.

How to Answer

Discuss the metrics you use to assess project success and how you align them with business objectives.

Example

“I measure success through key performance indicators (KPIs) relevant to the project goals. For instance, in a project aimed at increasing user retention, I tracked metrics like churn rate and user engagement levels post-implementation, which showed a 20% improvement.”

Communication & Stakeholder Management

6. Describe how you communicate complex technical concepts to non-technical stakeholders.

Effective communication is vital in a collaborative environment, and they will assess your ability to bridge the gap between technical and non-technical teams.

How to Answer

Provide examples of how you have simplified complex ideas and ensured understanding among diverse audiences.

Example

“I often use visual aids like graphs and dashboards to present data insights. In a recent meeting, I translated our model’s findings into actionable business strategies, ensuring that all stakeholders understood the implications without getting lost in technical jargon.”