State Of Minnesota Data Scientist Interview Questions + Guide in 2025

The State of Minnesota is dedicated to enhancing the quality of life for all its residents through effective policies and essential services.

As a Data Scientist within the Minnesota Department of Health, you will play a pivotal role in supporting syndromic surveillance initiatives. Your key responsibilities will include analyzing electronic health data to identify trends in hospital utilization and capacity, creating automated reporting systems, and publishing datasets and dashboards that inform public health actions. A strong foundation in statistical analysis, experience with programming and data visualization tools such as SAS, R, Tableau, or Power BI, and an understanding of epidemiological methods will be essential for success in this role. Additionally, the ability to communicate findings clearly and maintain effective relationships with health professionals is crucial, as this position involves collaboration with various stakeholders to enhance organizational capacity and inform public health strategies.

This guide aims to equip you with the necessary insights and preparation strategies to excel in your interview for the Data Scientist role at the State of Minnesota.

The interview process for a Data Scientist role at the State of Minnesota is structured and follows a multi-step format that emphasizes both technical and behavioral competencies.

1. Initial Application and Screening

Candidates begin by submitting their applications online. After a review period, selected candidates are contacted for an initial screening interview, which typically lasts around 30 minutes. This interview is often conducted via phone or video call and focuses on the candidate's background, motivations for applying, and alignment with the agency's values. Expect to discuss your experience in statistical analysis, data visualization, and any relevant projects that demonstrate your capabilities.

2. Behavioral Interview

Following the initial screening, candidates may participate in a behavioral interview. This round is designed to assess how candidates have handled various situations in the past, often using the "tell me about a time..." format. Interviewers will ask questions that evaluate your problem-solving skills, teamwork, and ability to manage conflicts. Be prepared to discuss specific examples from your previous work experiences, particularly those that highlight your analytical skills and ability to work independently.

3. Technical Assessment

Candidates who progress past the behavioral interview may be required to complete a technical assessment. This could involve a take-home exercise or a live coding session where you will demonstrate your proficiency in programming languages and statistical software relevant to the role, such as SAS, R, or Python. The assessment may also include tasks related to data analysis and visualization, requiring you to create reports or dashboards based on provided datasets.

4. Panel Interview

The final stage of the interview process typically involves a panel interview with multiple stakeholders, including team members and management. This interview lasts about an hour and delves deeper into your technical expertise, understanding of epidemiological methods, and ability to communicate findings effectively. Expect to answer questions that assess your knowledge of syndromic surveillance data and your experience with public health topics. Additionally, you may be asked about your approach to enhancing organizational capacity through data-driven insights.

Throughout the process, candidates should be prepared for a structured format where questions are often provided in advance, allowing for thoughtful responses. The interviewers will score your answers based on a standardized rubric, so clarity and relevance in your responses are crucial.

As you prepare for your interview, consider the types of questions that may be asked, focusing on your experiences and how they relate to the responsibilities of the Data Scientist role.

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

Prepare for a Structured Interview Format

The interview process at the State of Minnesota is known for its structured format, where candidates are asked the same set of questions. Familiarize yourself with common behavioral interview questions, particularly those that start with "Tell me about a time..." This will help you articulate your experiences clearly and concisely. Be ready to discuss your motivations for applying and how your background aligns with the role.

Emphasize Your DEI Awareness

Diversity, Equity, and Inclusion (DEI) are critical components of the State of Minnesota's hiring process. Expect to encounter questions that assess your understanding and commitment to these values. Prepare examples that demonstrate your ability to work in diverse teams and how you have contributed to advancing equity in your previous roles.

Showcase Your Technical Skills

As a Data Scientist, you will need to demonstrate your proficiency in statistical analysis and data visualization tools. Be prepared to discuss your experience with software such as SAS, R, Tableau, or Power BI. Highlight specific projects where you utilized these tools to derive insights or create impactful reports. This will not only showcase your technical skills but also your ability to apply them in real-world scenarios.

Be Ready for Behavioral Questions

Expect a mix of behavioral and technical questions during your interview. Prepare to discuss situations where you faced challenges, such as disagreements with supervisors or conflicts with coworkers. Use the STAR method (Situation, Task, Action, Result) to structure your responses, ensuring you convey the context, your role, and the outcomes of your actions.

Communicate Clearly and Effectively

Strong communication skills are essential for this role, as you will need to present complex data findings to various stakeholders. Practice articulating your thoughts clearly and concisely. Consider conducting mock interviews with a friend or mentor to refine your delivery and ensure you can explain technical concepts in an accessible manner.

Prepare Questions for Your Interviewers

At the end of your interview, you will likely have the opportunity to ask questions. Prepare thoughtful inquiries that demonstrate your interest in the role and the organization. Consider asking about the team dynamics, ongoing projects, or how the department measures success. This not only shows your enthusiasm but also helps you gauge if the organization aligns with your career goals.

Stay Patient and Follow Up

The interview process may take longer than expected, and communication can be slow. Be patient and proactive in following up after your interview. A polite email thanking your interviewers for their time and reiterating your interest in the position can leave a positive impression.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at the State of Minnesota. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at the State of Minnesota. The interview process is structured and may include a mix of behavioral and technical questions, focusing on your experience, problem-solving abilities, and understanding of data analysis in a public health context. Be prepared to discuss your past experiences and how they relate to the responsibilities of the role.

Behavioral Questions

1. Tell us about a time you had a conflict with your manager. What happened, and what did you learn?

This question assesses your conflict resolution skills and ability to learn from experiences.

How to Answer

Focus on a specific instance, describe the conflict, how you approached the situation, and what the outcome was. Highlight any lessons learned that improved your future interactions.

Example

“In a previous role, I disagreed with my manager on the approach to a data analysis project. I scheduled a meeting to discuss my concerns and presented data supporting my viewpoint. Ultimately, we reached a compromise that incorporated both our ideas, which led to a successful project outcome and improved our working relationship.”

2. Describe a time when you had to defend a decision that others opposed.

This question evaluates your ability to stand by your decisions and communicate effectively.

How to Answer

Choose an example where you had to advocate for a decision based on data or analysis. Explain how you communicated your reasoning and the eventual outcome.

Example

“I once proposed a new data collection method that my team was initially skeptical about. I presented the potential benefits and backed them with research. After implementing the method, we saw a significant increase in data accuracy, which ultimately won over my colleagues.”

3. How do you organize and prioritize your work?

This question gauges your time management and organizational skills.

How to Answer

Discuss your methods for prioritizing tasks, such as using project management tools or setting deadlines. Provide an example of how this has worked for you in the past.

Example

“I use a combination of task lists and project management software to prioritize my work. For instance, during a recent project, I identified critical tasks that needed immediate attention and scheduled them first, allowing me to meet the project deadline without compromising quality.”

4. Tell us about a project where time was of the essence. How did you manage to complete the project on time?

This question assesses your ability to work under pressure and meet deadlines.

How to Answer

Describe a specific project, the challenges you faced, and the strategies you employed to ensure timely completion.

Example

“During a public health initiative, I was tasked with analyzing data for a report due in a week. I broke the project into smaller tasks, delegated where possible, and set daily goals. By maintaining clear communication with my team, we completed the analysis ahead of schedule.”

Technical Questions

1. What statistical methods do you find most useful in data analysis, and why?

This question tests your knowledge of statistical techniques relevant to the role.

How to Answer

Discuss specific statistical methods you have used, their applications, and why they are effective in your work.

Example

“I frequently use regression analysis to identify trends and relationships in data. For instance, in a recent project analyzing healthcare utilization, regression helped me understand the impact of various factors on patient visits, allowing for more informed decision-making.”

2. How do you ensure data quality in your analyses?

This question evaluates your understanding of data integrity and quality assurance processes.

How to Answer

Explain the steps you take to validate and clean data before analysis, including any tools or techniques you use.

Example

“I implement a multi-step process for data quality assurance, including data validation checks, outlier detection, and cross-referencing with reliable sources. This ensures that the data I analyze is accurate and reliable, which is crucial for public health reporting.”

3. Can you describe your experience with data visualization tools? Which do you prefer and why?

This question assesses your familiarity with data visualization and your ability to communicate findings effectively.

How to Answer

Mention specific tools you have used, your preferred tool, and the reasons for your preference based on your experiences.

Example

“I have extensive experience with Tableau and Power BI. I prefer Tableau for its user-friendly interface and powerful visualization capabilities, which allow me to create interactive dashboards that effectively communicate complex data insights to stakeholders.”

4. What is your approach to analyzing syndromic surveillance data?

This question tests your specific knowledge related to the role and its responsibilities.

How to Answer

Discuss your understanding of syndromic surveillance, the types of data involved, and how you would approach analysis in this context.

Example

“My approach to analyzing syndromic surveillance data involves first understanding the context and objectives of the analysis. I would then clean and preprocess the data, apply appropriate statistical methods to identify trends, and create visualizations to communicate findings to public health officials for timely action.”