Milliman Data Scientist Interview Questions + Guide in 2025

Milliman is an independent consulting firm that delivers market-leading services and solutions to clients worldwide, focusing on critical issues such as retirement funding, healthcare financing, risk management, and data analytics.

As a Data Scientist at Milliman, you will play a vital role in analyzing and interpreting complex datasets, particularly within the healthcare sector. Your key responsibilities will include utilizing advanced data science techniques to derive insights from healthcare claims data and social determinants of health datasets. You will be tasked with developing and implementing data models and machine learning algorithms to support client needs, driving data-driven decision-making. Strong communication skills are essential, as you will need to create comprehensive reports and presentations to convey findings effectively to clients and internal stakeholders.

Success in this role requires a blend of technical expertise in programming languages such as Python and R, project management skills to handle multiple initiatives simultaneously, and the ability to engage with clients to foster strong relationships. Familiarity with healthcare regulations and data privacy standards is crucial, ensuring compliance in all analytical activities. Additionally, you should have a proactive approach to problem-solving and a commitment to maintaining high-quality data standards.

This guide will help you prepare for your job interview by providing insights into the core competencies and expectations for the Data Scientist role at Milliman, enabling you to showcase your skills and experiences effectively.

The interview process for a Data Scientist at Milliman is structured to assess both technical expertise and interpersonal skills, reflecting the company's commitment to delivering high-quality analytical services. The process typically unfolds in several key stages:

1. Application and Initial Screening

Candidates begin by submitting their applications through Milliman's website. After a few weeks, selected candidates receive an invitation to participate in an initial screening, which may involve a recorded video interview. This stage allows candidates to respond to a series of questions about their experience with programming languages such as Python, R, and SQL, as well as their familiarity with data analysis tools and methodologies. Candidates are given strict time limits for their responses, emphasizing the importance of concise communication.

2. Technical Interview

Following the initial screening, candidates who progress will participate in a technical interview. This interview is typically conducted via video conferencing and focuses on assessing the candidate's proficiency in data science techniques, including statistical analysis, machine learning, and data modeling. Candidates may be asked to solve coding problems or discuss their previous projects, particularly those involving R and its packages, as well as their experience with data governance and quality assurance practices.

3. Case Study Presentation

Candidates may be required to complete a case study as part of the interview process. This involves analyzing a dataset and presenting findings to the interview panel. The case study is designed to evaluate the candidate's analytical thinking, problem-solving abilities, and communication skills. Candidates should be prepared to discuss their approach to data-intensive problems and how they would apply their findings in a real-world context.

4. Onsite Interviews

The final stage of the interview process typically consists of onsite interviews, which can span an entire day. Candidates will meet with multiple interviewers from various departments, engaging in a series of one-on-one interviews. These sessions will cover a range of topics, including technical skills, project management experience, and client engagement strategies. Candidates should expect to discuss their leadership experience and how they have mentored junior team members in previous roles.

Throughout the interview process, candidates are encouraged to demonstrate their ability to communicate complex technical information to non-technical audiences, as well as their understanding of healthcare regulations and data privacy standards.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

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

Prepare for Video Interviews

Milliman utilizes a unique video interview process where you will record responses to various questions. Given the strict time limits for each response, practice articulating your thoughts clearly and concisely. Aim to convey your experience with programming languages like R, Python, and SQL, as well as your familiarity with healthcare data. Rehearse your answers to common questions, ensuring you can deliver them within the allotted time. This preparation will help you feel more confident and articulate during the actual recording.

Showcase Your Technical Expertise

As a Data Scientist at Milliman, you will be expected to demonstrate a strong command of programming languages, particularly R and Python. Be prepared to discuss your experience with data modeling, machine learning algorithms, and statistical analyses. Familiarize yourself with R packages and tools like R Shiny, as these are likely to come up in discussions. Additionally, brush up on your SQL skills, as technical interviews may include coding challenges that test your ability to manipulate and analyze data effectively.

Highlight Project Management Skills

Milliman values candidates who can manage multiple projects simultaneously. Be ready to discuss your project management experience, including how you prioritize tasks, meet deadlines, and ensure high-quality deliverables. Share specific examples of projects you've led or contributed to, emphasizing your ability to work collaboratively with cross-functional teams. This will demonstrate your readiness to take on the responsibilities of the role and your alignment with the company's collaborative culture.

Communicate Effectively

Strong communication skills are essential for a Data Scientist at Milliman, especially when conveying complex technical information to non-technical stakeholders. Practice explaining your past projects and methodologies in a way that is accessible to a broader audience. Prepare to discuss how you have engaged with clients in the past, focusing on your ability to build relationships and understand their needs. This will showcase your interpersonal skills and your fit within Milliman's client-focused environment.

Embrace the Company Culture

Milliman emphasizes a collaborative and innovative work environment. During your interview, express your enthusiasm for teamwork and your willingness to mentor junior team members. Share examples of how you've fostered collaboration in previous roles or how you've contributed to a positive team dynamic. This will resonate with the interviewers and demonstrate that you are a good cultural fit for the organization.

Stay Informed on Industry Trends

Milliman operates in a rapidly evolving field, particularly in healthcare analytics. Show your commitment to professional development by discussing recent trends, tools, or technologies in data science that you find interesting or relevant. This not only demonstrates your passion for the field but also your proactive approach to staying current, which is highly valued at Milliman.

By following these tips, you will be well-prepared to make a strong impression during your interview at Milliman. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Milliman. The interview process will likely assess your technical skills in programming, data analysis, and machine learning, as well as your ability to communicate complex information effectively. Be prepared to discuss your experience with healthcare data, project management, and client engagement.

Technical Skills

1. What is your experience with R, Python, and SQL?

Milliman values proficiency in programming languages, especially R and Python, for data analysis and modeling.

How to Answer

Discuss specific projects where you utilized these languages, highlighting your role and the outcomes achieved.

Example

“I have over five years of experience using R for statistical analysis and data visualization, particularly in healthcare projects. I also use Python for machine learning tasks, such as developing predictive models. In my last role, I utilized SQL to extract and manipulate large datasets, which improved our reporting efficiency by 30%.”

2. Can you explain a machine learning project you have worked on?

This question assesses your practical experience with machine learning algorithms and their application.

How to Answer

Describe the project, the problem you were solving, the algorithms you used, and the results.

Example

“I worked on a project to predict patient readmission rates using logistic regression. I gathered data from various sources, cleaned it, and built a model that achieved an accuracy of 85%. This model helped the hospital implement targeted interventions, reducing readmissions by 15%.”

3. How do you ensure data quality and integrity in your analyses?

Data quality is crucial in healthcare analytics, and Milliman will want to know your approach.

How to Answer

Discuss your methods for data validation, cleaning, and governance practices.

Example

“I implement a rigorous data validation process that includes checking for missing values, outliers, and inconsistencies. I also use automated scripts to monitor data quality over time, ensuring that our analyses are based on accurate and reliable data.”

4. Describe your experience with data visualization tools.

Milliman emphasizes the importance of effectively communicating data insights.

How to Answer

Mention specific tools you have used and how you applied them to present data.

Example

“I have extensive experience with Tableau and Power BI for data visualization. In my previous role, I created interactive dashboards that allowed stakeholders to explore key metrics in real-time, which facilitated data-driven decision-making across departments.”

5. What statistical methods do you commonly use in your analyses?

Understanding statistical methods is essential for a Data Scientist role.

How to Answer

List the statistical techniques you are familiar with and provide examples of how you have applied them.

Example

“I frequently use regression analysis, hypothesis testing, and A/B testing in my work. For instance, I conducted A/B testing to evaluate the effectiveness of a new patient outreach program, which helped us identify the most impactful strategies.”

Project Management and Client Engagement

1. How do you prioritize multiple projects with competing deadlines?

Milliman seeks candidates who can manage their time effectively.

How to Answer

Explain your approach to project management and prioritization.

Example

“I use a combination of project management tools and techniques, such as Agile methodologies, to prioritize tasks based on urgency and impact. I also maintain open communication with stakeholders to ensure alignment on priorities and deadlines.”

2. Describe a time when you had to communicate complex data findings to a non-technical audience.

Effective communication is key in this role.

How to Answer

Share an example that illustrates your ability to simplify complex information.

Example

“I presented a complex analysis of healthcare utilization trends to a group of state health officials. I used clear visuals and avoided jargon, focusing on the implications of the data for policy decisions. The presentation was well-received, and it led to actionable insights for their programs.”

3. How do you handle feedback from clients or stakeholders?

This question assesses your interpersonal skills and adaptability.

How to Answer

Discuss your approach to receiving and implementing feedback.

Example

“I view feedback as an opportunity for growth. I actively listen to client concerns and ask clarifying questions to fully understand their perspective. For example, after receiving feedback on a report, I revised it to better align with their expectations, which strengthened our relationship.”

4. Can you provide an example of a successful project you led?

Milliman values leadership and initiative.

How to Answer

Describe the project, your role, and the outcomes achieved.

Example

“I led a project to analyze social determinants of health data for a state health agency. I coordinated a team of analysts, developed the analytical framework, and presented our findings to the agency. The insights we provided helped them secure funding for targeted health initiatives.”

5. What strategies do you use to maintain strong client relationships?

Building and maintaining client relationships is crucial in this role.

How to Answer

Share your strategies for effective client engagement.

Example

“I prioritize regular communication and transparency with clients. I schedule periodic check-ins to discuss project progress and gather feedback. This approach has helped me build trust and foster long-term partnerships with clients.”