Unum Data Scientist Interview Questions + Guide in 2025

Unum is a Fortune 500 company dedicated to providing employee benefits and service solutions that empower individuals to thrive in both their professional and personal lives.

As a Data Scientist at Unum, you will play a crucial role in transforming complex data into actionable insights that directly impact business decisions. The position requires a strong foundation in programming, applied statistics, and data manipulation, as well as the ability to independently identify and execute data science strategies to support business objectives. You will be responsible for designing and implementing analytical solutions, integrating data from various sources, and constructing predictive models that drive business value. A proactive approach, curiosity, and a commitment to continuous learning are essential traits for success in this role, which aligns with Unum’s values of fostering an inclusive and supportive workplace culture.

This guide aims to help you prepare effectively for your interview by highlighting the key skills and competencies that Unum values in a Data Scientist, ensuring you can demonstrate your fit for both the role and the company culture.

The interview process for a Data Scientist II at Unum is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the role and the company culture. The process typically includes several stages:

1. Initial Phone Screen

The first step is a phone interview with a recruiter, lasting about 30 to 45 minutes. This conversation focuses on your background, experience, and motivation for applying to Unum. The recruiter will also discuss the company culture and the specifics of the role, gauging your fit for the organization.

2. Technical Assessment

Following the initial screen, candidates may be required to complete a technical assessment. This could involve a self-recorded video interview or a coding challenge that tests your proficiency in programming languages such as Python or R, as well as your understanding of statistics and machine learning concepts. Expect questions that require you to demonstrate your analytical skills and problem-solving abilities.

3. Panel Interview

The next stage is typically a panel interview conducted via video conferencing tools like Microsoft Teams. This interview usually involves two or more team members, including potential managers and peers. The panel will ask a mix of technical and behavioral questions, focusing on your past experiences, how you handle challenges, and your approach to teamwork. Be prepared to discuss specific projects you've worked on and how you applied data science techniques to solve business problems.

4. Final Interview

In some cases, a final interview may be conducted with senior leadership or additional team members. This round is often more focused on cultural fit and your long-term vision within the company. You may be asked about your career goals, how you handle stress, and your approach to mentoring or leading others in a team setting.

Throughout the process, candidates are encouraged to ask insightful questions about the role, team dynamics, and Unum's strategic goals, as this demonstrates your genuine interest in the position and the company.

Now that you have an understanding of the interview process, let's delve into the specific questions that candidates have encountered during their interviews at Unum.

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

Understand the Company Culture

Unum prides itself on an award-winning culture that emphasizes inclusion and diversity. Familiarize yourself with their values and how they translate into everyday practices. Be prepared to discuss how your personal values align with Unum's mission to help employees thrive. This understanding will not only help you answer questions more effectively but also demonstrate your genuine interest in being part of their team.

Prepare for Behavioral Questions

The interview process at Unum often includes behavioral questions. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on your past experiences, particularly those that showcase your problem-solving skills, ability to handle multiple projects, and how you’ve navigated conflicts in the workplace. Given the emphasis on teamwork and collaboration, be ready to share examples that highlight your interpersonal skills.

Brush Up on Technical Skills

As a Data Scientist, you will be expected to demonstrate proficiency in statistics, programming (especially Python), and SQL. Review key concepts in statistical modeling, machine learning algorithms, and data visualization techniques. Be prepared to discuss how you have applied these skills in previous roles, particularly in creating actionable insights from complex data sets. Practice articulating your thought process when solving technical problems, as this will be crucial during technical interviews.

Communicate Clearly and Confidently

Effective communication is vital, especially when discussing complex data findings with non-technical stakeholders. Practice explaining your analytical processes and results in a clear and concise manner. Be ready to discuss how you would present your findings to influence decision-making at various levels of the organization. This will demonstrate your ability to bridge the gap between data science and business strategy.

Show Initiative and Curiosity

Unum values proactive individuals who take the initiative to identify opportunities for improvement. During your interview, express your enthusiasm for continuous learning and your desire to contribute to the organization’s success. Share examples of how you have independently pursued projects or learning opportunities in the past, and how that has benefited your previous employers.

Prepare Thoughtful Questions

At the end of your interview, you will likely have the opportunity to ask questions. Prepare thoughtful inquiries that reflect your interest in the role and the company. Consider asking about the team dynamics, the types of projects you would be working on, or how Unum measures success in the Data Science team. This not only shows your interest but also helps you assess if the company is the right fit for you.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the position and briefly mention a key point from your conversation that reinforces your fit for the role. This small gesture can leave a positive impression and keep you top of mind as they make their decision.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Unum. The interview process will likely focus on your technical expertise in programming, applied statistics, data manipulation, and your ability to derive actionable insights from complex data. Be prepared to discuss your experience with statistical modeling, data visualization, and problem-solving in a business context.

Statistics & Statistical Modeling

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

Understanding the distinction between these two types of machine learning is crucial for a data scientist.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight scenarios where one might be preferred over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like customer segmentation in marketing data.”

2. Describe a statistical model you have built in the past. What was the outcome?

This question assesses your practical experience with statistical modeling.

How to Answer

Detail the model you built, the data used, the methodology, and the results. Emphasize the impact of your work on the business.

Example

“I developed a logistic regression model to predict customer churn for a subscription service. By analyzing customer behavior data, the model identified key factors influencing churn, allowing the marketing team to implement targeted retention strategies, which reduced churn by 15%.”

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

Handling missing data is a common challenge in data science.

How to Answer

Discuss various techniques for dealing with 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 based on other features in the dataset.”

4. What is the purpose of feature selection, and how do you approach it?

Feature selection is critical for improving model performance.

How to Answer

Explain the importance of selecting relevant features and describe your process for feature selection, including any techniques you use.

Example

“Feature selection helps reduce overfitting and improves model interpretability. I often use techniques like Recursive Feature Elimination (RFE) or feature importance from tree-based models to identify and retain the most impactful features.”

Machine Learning

1. Can you walk us through the process of building a machine learning model?

This question evaluates your understanding of the machine learning lifecycle.

How to Answer

Outline the steps from problem definition to model deployment, emphasizing your experience in each phase.

Example

“I start by defining the problem and gathering relevant data. After preprocessing the data, I select appropriate algorithms and train the model. I then evaluate its performance using metrics like accuracy or F1 score, and finally, I deploy the model and monitor its performance in production.”

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

This question assesses your technical expertise in machine learning.

How to Answer

List the algorithms you are familiar with and provide examples of when you have used them.

Example

“I am comfortable with a range of algorithms, including linear regression, decision trees, and support vector machines. For instance, I used a decision tree model to classify customer feedback into positive and negative categories, which helped the product team prioritize improvements.”

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

Understanding model evaluation is key to ensuring its effectiveness.

How to Answer

Discuss various evaluation metrics and when to use them, as well as any validation techniques you employ.

Example

“I evaluate model performance using metrics like accuracy, precision, recall, and AUC-ROC, depending on the problem type. I also use cross-validation to ensure the model generalizes well to unseen data.”

4. Explain the concept of overfitting and how to prevent it.

Overfitting is a common issue in machine learning that can lead to poor model performance.

How to Answer

Define overfitting and discuss strategies to mitigate it, such as regularization or cross-validation.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation, pruning in decision trees, and regularization methods like Lasso or Ridge regression.”

Data Manipulation & SQL

1. How do you approach writing complex SQL queries?

This question assesses your SQL skills and ability to manipulate data.

How to Answer

Describe your process for constructing SQL queries, including how you handle joins and aggregations.

Example

“I start by clearly defining the data I need and the relationships between tables. I use JOINs to combine data from multiple sources and apply GROUP BY and aggregate functions to summarize the results. For instance, I wrote a query to analyze sales data across different regions, which involved multiple joins and aggregations.”

2. Can you explain the difference between INNER JOIN and LEFT JOIN?

Understanding SQL joins is fundamental for data extraction.

How to Answer

Define both types of joins and provide examples of when to use each.

Example

“An INNER JOIN returns only the rows with matching values in both tables, while a LEFT JOIN returns all rows from the left table and matched rows from the right table, filling in NULLs where there are no matches. I use INNER JOIN when I need only related records, and LEFT JOIN when I want to retain all records from the primary table.”

3. Describe a time when you had to troubleshoot a complex SQL query.

This question evaluates your problem-solving skills in SQL.

How to Answer

Share a specific example of a challenging query you encountered and how you resolved the issue.

Example

“I once faced a performance issue with a query that involved multiple joins and subqueries. I analyzed the execution plan and identified that adding indexes on key columns significantly improved the query’s performance, reducing execution time from several minutes to under 10 seconds.”

4. How do you ensure data quality when working with large datasets?

Data quality is crucial for accurate analysis.

How to Answer

Discuss your strategies for validating and cleaning data to maintain quality.

Example

“I implement data validation checks during the data ingestion process, such as verifying data types and checking for duplicates. Additionally, I perform exploratory data analysis to identify anomalies and outliers, ensuring the dataset is clean and reliable for analysis.”