UNC Health Care Data Scientist Interview Questions + Guide in 2025

UNC Health Care is dedicated to improving the health and well-being of diverse communities through a comprehensive healthcare system.

As a Data Scientist at UNC Health Care, you will be at the forefront of leveraging advanced analytical methods to enhance healthcare delivery, cost-efficiency, quality, and patient outcomes. This role entails leading the design, execution, and interpretation of analytical studies, as well as creating innovative analytical solutions that provide actionable insights to stakeholders. You will be responsible for developing enterprise-class analytic models, managing the full lifecycle of analytical solution development, and ensuring efficient, automated processes for large-scale data analyses. A strong background in statistics and machine learning, coupled with proficiency in programming languages such as Python and R, is essential to thrive in this role. Furthermore, your ability to communicate complex data-driven insights clearly to non-technical audiences and collaborate effectively with diverse teams will align perfectly with UNC Health Care's commitment to inclusivity and excellence.

This guide aims to equip candidates with the knowledge and confidence needed to excel in interviews for the Data Scientist role at UNC Health Care.

The interview process for a Data Scientist at UNC Health Care is structured and thorough, reflecting the organization's commitment to hiring excellence. The process typically unfolds over several weeks and consists of multiple stages designed to assess both technical and behavioral competencies.

1. Application and Initial Screening

Candidates begin by submitting their applications through the UNC Health portal. Following this, an initial phone screening is conducted, usually lasting around 30 minutes. During this call, a recruiter will discuss the role, gauge the candidate's interest in UNC Health, and explore their relevant experience in data analysis and statistics.

2. Technical Assessment

After the initial screening, candidates may be required to complete a technical assessment, which often includes an Excel assessment. This step is crucial for evaluating the candidate's proficiency in data manipulation and analysis, as well as their familiarity with statistical methods and tools.

3. Behavioral Interviews

Candidates who pass the technical assessment will move on to a series of behavioral interviews. These interviews typically involve a panel of interviewers, including team members and hiring managers. The focus here is on understanding the candidate's past experiences and how they align with the organization's values. Interviewers often employ the STAR (Situation, Task, Action, Result) method to elicit detailed responses about the candidate's problem-solving abilities and teamwork experiences.

4. Presentation Round

In some cases, candidates may be asked to prepare and deliver a presentation on a relevant topic. This stage assesses the candidate's ability to communicate complex ideas effectively and engage an audience, which is essential for a Data Scientist who must convey analytical insights to stakeholders.

5. Final Interviews

The final round typically consists of one-on-one interviews with senior leadership or key stakeholders. These interviews may delve deeper into the candidate's technical expertise, including their knowledge of statistical methods, machine learning, and programming languages such as Python and SQL. Candidates should be prepared to discuss their approach to data analysis and how they would contribute to the organization's goals.

Throughout the process, candidates should expect a thorough evaluation of their skills and experiences, as well as their fit within the UNC Health Care culture.

Next, let's explore the specific interview questions that candidates have encountered during this process.

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

Understand the Interview Structure

The interview process at UNC Health Care can be lengthy and may involve multiple rounds, including phone screenings, behavioral interviews, and technical assessments. Be prepared for a panel interview format where you may face a mix of HR representatives, department managers, and team members. Familiarize yourself with the STAR method (Situation, Task, Action, Result) to effectively articulate your experiences during behavioral questions. This structured approach will help you convey your thought process and problem-solving skills clearly.

Showcase Your Technical Expertise

As a Data Scientist, you will be expected to demonstrate a strong command of statistical methods, algorithms, and programming languages such as Python and SQL. Brush up on your knowledge of advanced analytics, including machine learning and data mining techniques. Be ready to discuss your experience with data analysis projects, focusing on how you applied statistical methods to derive insights and solve complex problems. Highlight any experience you have with deep learning models or generative AI, as these are increasingly relevant in the healthcare analytics space.

Prepare for Behavioral Questions

Expect to answer questions that assess your ability to work in a team, manage competing priorities, and handle difficult situations. Reflect on your past experiences and prepare specific examples that illustrate your problem-solving skills and adaptability. Questions like "Tell me about a time you dealt with a difficult team member" or "How do you manage competing priorities?" are common, so practice articulating your responses using the STAR method to ensure clarity and impact.

Emphasize Your Passion for Healthcare

UNC Health Care is dedicated to improving the health and well-being of the communities it serves. Demonstrating a genuine interest in healthcare analytics and how your work can contribute to better health outcomes will resonate well with your interviewers. Be prepared to discuss why you want to work at UNC Health specifically and how your values align with the organization's mission.

Be Ready for Technical Assessments

You may encounter technical assessments during the interview process, such as an Excel assessment or a presentation on a relevant topic. Practice your technical skills in Excel, focusing on data manipulation and analysis techniques. If asked to present, choose a topic that showcases your analytical abilities and engages your audience. Aim to convey complex information in a clear and compelling manner, as effective communication is crucial in this role.

Stay Professional and Patient

The hiring process at UNC Health Care can be slow, with multiple interviews and assessments. Maintain professionalism throughout the process, even if it feels repetitive or lengthy. Patience and a positive attitude will reflect well on you as a candidate. Remember that the organization values thoroughness in its hiring process, which is indicative of its commitment to finding the right fit for the team.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at UNC Health Care. The interview process will likely focus on a combination of technical skills, statistical knowledge, and behavioral competencies. Candidates should be prepared to demonstrate their expertise in data analysis, machine learning, and their ability to communicate complex concepts effectively.

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each method is best suited for.

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 or groupings, like customer segmentation in marketing.”

2. Describe a machine learning project you have worked on. What was your approach?

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project’s objective, the data you used, the algorithms you implemented, and the results you achieved.

Example

“I worked on a project to predict patient readmission rates. I collected historical patient data, performed feature engineering, and used logistic regression to model the likelihood of readmission. The model improved our predictions by 20%, allowing for better resource allocation.”

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 to estimate missing values or consider dropping those records if they are not critical to the analysis.”

4. What is cross-validation, and why is it important?

This question tests your understanding of model evaluation techniques.

How to Answer

Explain the concept of cross-validation and its role in assessing model performance and preventing overfitting.

Example

“Cross-validation is a technique used to evaluate a model’s performance by partitioning the data into subsets. It helps ensure that the model generalizes well to unseen data, as it tests the model on different data splits, providing a more reliable estimate of its accuracy.”

5. Can you explain a time when you had to optimize a model? What steps did you take?

This question evaluates your analytical and optimization skills.

How to Answer

Describe the model you were optimizing, the metrics you used to measure performance, and the specific techniques you applied.

Example

“I was tasked with optimizing a predictive model for patient outcomes. I started by analyzing feature importance and removed less significant variables. Then, I experimented with different algorithms and hyperparameters using grid search, which ultimately improved the model’s accuracy by 15%.”

Statistics & Probability

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

This question assesses your statistical knowledge relevant to data science.

How to Answer

List the statistical methods you are familiar with and provide context on how you have applied them in your work.

Example

“I frequently use regression analysis for predictive modeling, along with hypothesis testing to validate my findings. Additionally, I apply techniques like ANOVA for comparing means across groups and clustering methods for segmentation tasks.”

2. How do you interpret p-values in the context of hypothesis testing?

Understanding p-values is essential for statistical analysis.

How to Answer

Explain what a p-value represents and how it influences decision-making in hypothesis testing.

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, typically below 0.05, suggests that we can reject the null hypothesis, indicating a statistically significant effect.”

3. Can you explain the concept of confidence intervals?

This question tests your understanding of statistical inference.

How to Answer

Define confidence intervals and their significance in estimating population parameters.

Example

“A confidence interval provides a range of values within which we expect the true population parameter to lie, with a certain level of confidence, usually 95%. It helps quantify the uncertainty around our estimates.”

4. What is the Central Limit Theorem, and why is it important?

This question assesses your grasp of fundamental statistical principles.

How to Answer

Discuss the Central Limit Theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters based on sample data.”

5. How do you assess the normality of a dataset?

This question evaluates your ability to analyze data distributions.

How to Answer

Describe the methods you use to check for normality, such as visualizations and statistical tests.

Example

“I assess normality using visual methods like Q-Q plots and histograms, along with statistical tests like the Shapiro-Wilk test. If the data is not normally distributed, I consider transformations or non-parametric methods for analysis.”

Behavioral Questions

1. Describe a time you dealt with a difficult team member. What did you do?

This question evaluates your interpersonal skills and conflict resolution abilities.

How to Answer

Use the STAR method to structure your response, focusing on the situation, your actions, and the outcome.

Example

“In a previous project, a team member was resistant to feedback. I scheduled a one-on-one meeting to understand their perspective and collaboratively set clear expectations. This open dialogue improved our working relationship and ultimately enhanced the project’s success.”

2. How do you manage competing priorities?

This question assesses your organizational and time management skills.

How to Answer

Discuss your approach to prioritization and time management, providing an example if possible.

Example

“I prioritize tasks based on urgency and impact. For instance, during a project with tight deadlines, I created a detailed schedule, breaking down tasks into manageable parts, which allowed me to meet all deadlines without compromising quality.”

3. Why do you want to leave your current job? Why UNC?

This question gauges your motivations and fit for the organization.

How to Answer

Be honest about your reasons for seeking new opportunities while expressing enthusiasm for UNC Health Care’s mission and values.

Example

“I’m looking for a role that allows me to apply my data science skills in a meaningful way, particularly in healthcare. UNC Health Care’s commitment to improving community health aligns with my values, and I’m excited about the opportunity to contribute to impactful projects.”

4. Give an example of a time you missed a deadline.

This question evaluates your accountability and learning from mistakes.

How to Answer

Describe the situation, what led to the missed deadline, and what you learned from the experience.

Example

“I once underestimated the time required for data cleaning in a project, which led to a missed deadline. I took responsibility, communicated with my team, and we adjusted our timeline. Since then, I’ve improved my project planning skills to better account for unexpected challenges.”

5. Where do you see yourself in five years?

This question assesses your career aspirations and alignment with the company’s goals.

How to Answer

Discuss your professional goals and how they relate to the role and organization.

Example

“In five years, I see myself as a lead data scientist, driving innovative projects that leverage data to improve patient outcomes. I believe UNC Health Care’s focus on analytics will provide the perfect environment for me to grow and contribute to meaningful advancements in healthcare.”