Voya Financial Machine Learning Engineer Interview Questions + Guide in 2025

Voya Financial is dedicated to helping individuals and institutions achieve their financial goals through innovative solutions and services.

As a Machine Learning Engineer at Voya Financial, you will be responsible for designing, implementing, and optimizing machine learning models that drive insights and enhance decision-making processes within the organization. Key responsibilities include developing algorithms to analyze large datasets, collaborating with cross-functional teams to translate business requirements into technical solutions, and ensuring the scalability and robustness of machine learning applications. Required skills include proficiency in programming languages such as Python or R, experience with data manipulation tools like SQL or Tableau, and a strong understanding of machine learning frameworks and algorithms. Additionally, a great fit for this role possesses excellent problem-solving abilities, strong communication skills to convey complex concepts to non-technical stakeholders, and a passion for leveraging data to create impactful financial solutions.

This guide will help you prepare effectively for your interview by providing insights into the specific skills and experiences that Voya Financial values in their Machine Learning Engineers, enabling you to present yourself as a strong candidate.

The interview process for a Machine Learning Engineer at Voya Financial is structured to assess both technical skills and cultural fit within the organization. The process typically consists of several key stages:

1. Phone Screening

The initial step is a phone screening, which usually lasts about 30 to 45 minutes. During this conversation, candidates will engage with hiring managers who will inquire about their previous work experiences and relevant projects. This stage is designed to be conversational, allowing candidates to showcase their background while also gaining insights into the role and the team dynamics.

2. Technical Interview

Following the phone screening, candidates will participate in a technical interview, which is conducted virtually and lasts approximately one hour. This interview focuses on assessing the candidate's proficiency in machine learning concepts, programming skills, and data manipulation techniques. Expect questions related to SQL, data visualization tools like Tableau, and specific machine learning algorithms. Candidates may also be asked to solve practical problems or case studies relevant to the role.

3. Behavioral Interview

The final stage of the interview process is a behavioral interview, typically lasting one hour and conducted with a manager and a senior analyst. This interview aims to evaluate the candidate's soft skills, teamwork, and alignment with Voya Financial's values. Candidates should be prepared to discuss past experiences, challenges faced in previous roles, and how they approach collaboration and problem-solving in a team environment.

As you prepare for your interview, consider the types of questions that may arise in these stages, particularly those that delve into your technical expertise and behavioral competencies.

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

Understand the Role and Its Impact

As a Machine Learning Engineer at Voya Financial, your work will directly influence the company's ability to leverage data for better decision-making. Familiarize yourself with the specific projects and initiatives that the team is currently working on. This will not only help you understand the technical requirements but also allow you to articulate how your skills and experiences align with the company's goals.

Prepare for Technical Assessments

Expect a strong focus on technical skills during the interview process. Brush up on SQL and data visualization tools like Tableau, as these are crucial for data manipulation and presentation. Be prepared to discuss the differences between various SQL joins and demonstrate your understanding of data structures. Additionally, review machine learning concepts and algorithms, as you may be asked to explain how you would apply them to real-world problems.

Embrace the Conversational Nature of Interviews

The interview process at Voya Financial tends to be more conversational, especially during the phone screening. Approach these discussions as opportunities to showcase your personality and communication skills. Be ready to discuss your previous projects in detail, including the artifacts you created and the impact they had. This will help you build rapport with the interviewers and demonstrate your collaborative spirit.

Highlight Your Problem-Solving Skills

In both technical and behavioral interviews, emphasize your problem-solving abilities. Be prepared to discuss specific challenges you've faced in past projects and how you overcame them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate the context and your contributions.

Align with Company Culture

Voya Financial values collaboration, innovation, and a commitment to customer service. Reflect on how your personal values align with these principles and be ready to share examples that demonstrate your fit within the company culture. Show enthusiasm for working in a team-oriented environment and your willingness to contribute to the company's mission.

Follow Up Thoughtfully

After your interviews, take the time to send a thoughtful follow-up email to express your gratitude for the opportunity to interview. Mention specific topics discussed during the interview that resonated with you, reinforcing your interest in the role and the company. This small gesture can leave a lasting impression and set you apart from other candidates.

By preparing thoroughly and approaching the interview with confidence and authenticity, you'll position yourself as a strong candidate for the Machine Learning Engineer role at Voya Financial. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Voya Financial. The interview process will likely assess your technical skills in machine learning, data manipulation, and your ability to communicate effectively with both technical and non-technical stakeholders. Be prepared to discuss your previous experiences and how they relate to the projects you may work on at Voya.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial, as it forms the basis of many applications in the field.

How to Answer

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

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification tasks using algorithms like decision trees. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, such as clustering with K-means.”

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 in real-world applications.

How to Answer

Discuss the project’s objectives, your role, the challenges encountered, and how you overcame them. Emphasize the impact of your work.

Example

“I worked on a predictive maintenance project for manufacturing equipment. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. This improved our model's accuracy by 15%, leading to significant cost savings for the company.”

Data Manipulation and SQL

3. What are the different types of SQL joins, and when would you use each?

This question tests your knowledge of SQL, which is essential for data manipulation in machine learning projects.

How to Answer

Explain the different types of joins (INNER, LEFT, RIGHT, FULL) and provide examples of when each would be appropriate in a data analysis context.

Example

“INNER JOIN returns records with matching values in both tables, while LEFT JOIN returns all records from the left table and matched records from the right. I would use INNER JOIN when I need only the intersecting data, and LEFT JOIN when I want to retain all records from the primary dataset.”

4. How do you handle missing or corrupted data in a dataset?

Data quality is critical in machine learning, and this question evaluates your approach to data preprocessing.

How to Answer

Discuss various techniques for handling missing data, such as imputation, removal, 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 analyze the data patterns and consider using predictive models to fill in the missing values, ensuring that the integrity of the dataset is maintained.”

Behavioral and Communication Skills

5. Describe a time when you had to explain a complex technical concept to a non-technical audience.

This question gauges your communication skills and ability to bridge the gap between technical and non-technical stakeholders.

How to Answer

Provide a specific example where you successfully communicated a complex idea, focusing on your approach and the outcome.

Example

“I once presented a machine learning model to a group of marketing professionals. I simplified the technical jargon by using analogies and visual aids, which helped them understand the model's impact on customer segmentation. This led to their enthusiastic support for implementing the model in our marketing strategy.”

6. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills, which are vital in a fast-paced environment.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use to manage your workload effectively.

Example

“I prioritize tasks based on their impact and deadlines. I use a combination of the Eisenhower Matrix and project management tools like Trello to visualize my workload. This helps me focus on high-impact tasks while ensuring that I meet all deadlines.”