Intermountain Healthcare Data Scientist Interview Questions + Guide in 2025

Intermountain Healthcare is a leading non-profit healthcare system dedicated to providing high-quality care and improving patient outcomes through innovative solutions and data-driven decision-making.

The Data Scientist role at Intermountain Healthcare is pivotal for harnessing data to enhance healthcare services and drive operational efficiency. Key responsibilities include analyzing complex datasets, developing predictive models, and generating actionable insights that inform clinical and operational strategies. A successful candidate should possess strong skills in Python programming, algorithms, and machine learning, alongside an ability to interpret and communicate analytical results effectively. Familiarity with healthcare data, including patient outcomes and clinical metrics, is highly valued, as is an inquisitive mindset that thrives on solving intricate problems. Ideal candidates will demonstrate resilience and adaptability in navigating the challenges of the healthcare landscape, aligning with Intermountain's commitment to continuous improvement and patient-centered care.

This guide will help you prepare for a job interview by providing insights into the expectations and focus areas for the Data Scientist role, equipping you with the knowledge to showcase your skills and fit for the company.

The interview process for a Data Scientist at Intermountain Healthcare is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. Application and Automated Screening

Candidates begin by submitting their applications online. Following this, they may be prompted to complete an automated video interview through a platform like HireVue. This initial screening often includes basic human resource questions designed to gauge the candidate's background and motivations. However, the experience can feel impersonal, as candidates may not have the opportunity to engage in a two-way dialogue about the role or the company.

2. Technical Assignment

After the automated screening, candidates may receive a programming assignment that is expected to be completed within a tight timeframe. This assignment is designed to evaluate the candidate's technical proficiency, particularly in programming languages relevant to data science, such as Python or R. Candidates should be prepared for the possibility of encountering intentional errors within the assignment, which may test their problem-solving skills and attention to detail.

3. Phone Interview

Following the completion of the technical assignment, candidates typically participate in a phone interview. This conversation often focuses on the candidate's technical skills, including their experience with data modeling and analytics. Candidates may be presented with a case study involving medical data, requiring them to demonstrate their analytical thinking and problem-solving abilities in real-time.

4. One-on-One Technical Interview

The final stage usually consists of a one-on-one technical interview, where candidates engage in a deeper discussion about their technical expertise. This may involve answering specific technical questions and working through a case study that requires the application of statistical methods and modeling techniques. Candidates should be ready to articulate their thought processes and explain their approach to solving complex data-related problems.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during these stages.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Intermountain Healthcare. The interview process will likely assess your technical skills in programming, machine learning, and data analysis, as well as your ability to apply these skills to real-world healthcare data challenges. Be prepared to discuss your experience with statistical modeling, algorithms, and how you approach problem-solving in a healthcare context.

Technical Skills

1. What programming languages are you most comfortable with, and how have you used them in your previous projects?

Intermountain Healthcare values proficiency in programming languages, particularly Python and R, for data analysis and modeling tasks.

How to Answer

Discuss your experience with specific programming languages, highlighting projects where you applied these skills to solve data-related problems.

Example

“I am most comfortable with Python, which I used extensively in my last project to analyze patient data and build predictive models. I also have experience with R for statistical analysis, particularly in a case study where I modeled healthcare outcomes based on various patient metrics.”

2. Can you explain a machine learning algorithm you have implemented and the results it produced?

Understanding machine learning algorithms is crucial for a Data Scientist role, especially in a healthcare setting.

How to Answer

Choose a specific algorithm, explain its purpose, how you implemented it, and the impact it had on your project.

Example

“I implemented a random forest algorithm to predict patient readmission rates. By training the model on historical data, we achieved an accuracy of 85%, which allowed the healthcare team to identify high-risk patients and tailor their post-discharge plans accordingly.”

Statistics and Probability

3. Describe a situation where you had to deal with multicollinearity in your data. How did you handle it?

Multicollinearity can significantly affect the performance of statistical models, making this a relevant question for the role.

How to Answer

Explain your understanding of multicollinearity and the techniques you used to address it in your analysis.

Example

“In a recent project, I encountered multicollinearity when analyzing patient demographics and health outcomes. I used variance inflation factors to identify the problematic variables and decided to remove one of the correlated predictors, which improved the model’s interpretability and performance.”

4. How do you ensure the validity and reliability of your statistical models?

This question assesses your approach to maintaining high standards in your analytical work.

How to Answer

Discuss the methods you use to validate your models, such as cross-validation, and how you ensure the data quality.

Example

“I ensure the validity of my models by using k-fold cross-validation to assess their performance on unseen data. Additionally, I always check for data quality issues, such as missing values or outliers, before proceeding with any analysis.”

Problem-Solving and Case Studies

5. Tell us about a time you were given a problem you didn't know how to solve. How did you approach it?

This question evaluates your problem-solving skills and your ability to learn and adapt.

How to Answer

Share a specific example, focusing on your thought process and the steps you took to find a solution.

Example

“When faced with a complex modeling problem involving a new dataset, I first conducted a thorough exploratory data analysis to understand its structure. I then researched similar cases and consulted with colleagues, which led me to a novel approach that ultimately resolved the issue.”

6. How would you approach a case study involving healthcare data with missing values?

This question tests your analytical thinking and familiarity with common data issues in healthcare.

How to Answer

Outline your strategy for handling missing data, including any techniques you would apply.

Example

“I would first assess the extent and pattern of the missing values. Depending on the situation, I might use imputation techniques, such as mean or median substitution, or more advanced methods like multiple imputation. If the missing data is significant, I would also consider analyzing the impact of missingness on the results.”

Algorithms and Data Analysis

7. What is your experience with algorithms in data analysis, and can you provide an example of one you used effectively?

This question focuses on your understanding of algorithms and their application in data analysis.

How to Answer

Discuss a specific algorithm, how you implemented it, and the results it yielded.

Example

“I have experience with decision tree algorithms, which I used in a project to classify patients based on their risk factors for chronic diseases. The decision tree provided clear insights into the most significant predictors, which helped the healthcare team prioritize interventions for at-risk patients.”

8. How do you approach feature selection in your models?

Feature selection is critical for building effective models, and this question assesses your methodology.

How to Answer

Explain your process for selecting features, including any tools or techniques you use.

Example

“I approach feature selection by first using correlation analysis to identify relationships between features and the target variable. I also apply techniques like recursive feature elimination and regularization methods to refine my feature set, ensuring that I retain only the most impactful variables for my models.”