Change Healthcare Data Scientist Interview Questions + Guide in 2025

Change Healthcare is a leading healthcare technology company dedicated to transforming the healthcare system through innovative solutions that enhance patient care, streamline financial processes, and improve clinical decision-making.

As a Data Scientist at Change Healthcare, you will be pivotal in interpreting complex data to derive actionable insights that drive business decisions. Your key responsibilities will include collaborating with various stakeholders to identify business requirements, engineering and modeling data from diverse sources, and developing predictive models using machine learning techniques. A strong background in Python and SQL is essential, as well as experience in analytics and machine learning. You'll be expected to leverage your skills to create data-driven solutions that address customer needs and improve operational efficiencies, aligning with Change Healthcare's mission to inspire a better healthcare system.

This guide will help you prepare for your interview by providing insights into the skills and knowledge necessary for success in this role, as well as the company culture at Change Healthcare.

The interview process for a Data Scientist role at Change Healthcare is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Screening

The process begins with an initial screening conducted by a recruiter, which usually takes place over the phone. This conversation focuses on your background, experience, and motivation for applying to Change Healthcare. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, ensuring that candidates have a clear understanding of what to expect.

2. Technical Screening

Following the initial screening, candidates will undergo a technical interview, often conducted via video conferencing tools like Microsoft Teams. This stage is designed to evaluate your proficiency in essential skills such as Python, SQL, and machine learning techniques. Expect to discuss your experience with data analysis, data engineering, and the application of machine learning algorithms. You may also be asked to solve practical problems or case studies relevant to the healthcare domain.

3. Onsite Interview

The onsite interview is a more in-depth assessment that typically includes multiple rounds with various team members. During these sessions, you will be evaluated on your technical expertise, problem-solving abilities, and how well you can communicate complex data-driven insights. The interviews may cover topics such as statistical techniques, data modeling, and the development of predictive models. Additionally, behavioral questions will be included to gauge your fit within the team and the broader company culture.

4. Offer and Negotiation

If you successfully navigate the previous stages, you will receive a job offer. This stage includes discussions about compensation, benefits, and any other terms of employment. Change Healthcare values transparency and open communication, so be prepared to negotiate based on your research and expectations.

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

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

Understand the Company’s Mission and Values

Change Healthcare is dedicated to improving the healthcare system through innovative technology solutions. Familiarize yourself with their mission to inspire a better healthcare system and how your role as a Data Scientist can contribute to this goal. Be prepared to discuss how your skills and experiences align with their mission, and think about specific examples where you have made a positive impact in previous roles.

Prepare for a Rigorous Interview Process

The interview process at Change Healthcare typically includes an HR screening, a technical screen, and an onsite interview. Given the emphasis on technical skills, ensure you are well-versed in Python, SQL, and machine learning concepts. Practice articulating your thought process clearly, as the interviewers may focus on your problem-solving approach rather than just the final answer.

Showcase Your Technical Proficiency

As a Data Scientist, you will be expected to demonstrate a strong command of data analysis, machine learning, and data engineering. Brush up on your knowledge of statistical techniques, data modeling, and algorithms. Be ready to discuss your experience with tools like Jupyter Notebook, Spark, and AWS, as these are part of the tech stack used at Change Healthcare. Consider preparing a portfolio of projects that highlight your technical skills and problem-solving abilities.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your ability to work collaboratively across functional teams. Change Healthcare values strong communication skills, so be prepared to share examples of how you have effectively communicated complex data insights to non-technical stakeholders. Highlight your experience in leading projects and how you have navigated challenges in a team setting.

Emphasize Innovation and Problem-Solving

The Data Science team at Change Healthcare focuses on innovation and problem-solving. Be prepared to discuss how you approach data-driven challenges and your experience in developing custom algorithms or predictive models. Share specific examples of how you have identified trends or insights from data that led to actionable business recommendations.

Stay Professional and Composed

While some candidates have reported less-than-ideal experiences with interviewers, it’s essential to remain professional and composed throughout the process. If faced with challenging questions or a difficult interviewer, maintain your confidence and focus on showcasing your skills and experiences. Remember, the interview is as much about you assessing the company as it is about them assessing you.

Reflect on Diversity and Inclusion

Change Healthcare places a strong emphasis on diversity and inclusion. Be prepared to discuss how you value diverse perspectives and how you have contributed to an inclusive work environment in your previous roles. This can be a great opportunity to demonstrate your alignment with the company’s culture and values.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Scientist role at Change Healthcare. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Change Healthcare. The interview process will assess your technical skills in data analysis, machine learning, and programming, as well as your ability to communicate insights effectively to stakeholders. Be prepared to demonstrate your understanding of healthcare data challenges and how your analytical skills can drive business solutions.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role, as you will be expected to apply various techniques to solve business problems.

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 using regression for predicting sales. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

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

This question assesses your practical experience and problem-solving skills in applying machine learning techniques.

How to Answer

Outline the problem, your approach to data collection and preprocessing, the algorithms you chose, and the results you achieved.

Example

“I worked on a project to predict patient readmission rates. I gathered historical patient data, cleaned it, and used logistic regression to model the likelihood of readmission. The model improved our readmission prediction accuracy by 20%, allowing for better resource allocation.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and optimization techniques.

How to Answer

Discuss various strategies to prevent overfitting, such as cross-validation, regularization, and pruning.

Example

“To combat overfitting, I use techniques like cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization methods like Lasso or Ridge regression to penalize overly complex models.”

4. What is feature engineering, and why is it important?

Feature engineering is a critical step in the data science process, and understanding its significance is essential.

How to Answer

Explain what feature engineering entails and how it can impact model performance.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. It’s crucial because well-engineered features can significantly enhance the model’s ability to learn patterns, leading to better predictions.”

Statistics & Probability

1. Explain the concept of p-value and its significance in hypothesis testing.

A solid grasp of statistical concepts is vital for interpreting data and making informed decisions.

How to Answer

Define p-value and discuss its role in determining statistical significance.

Example

“The p-value measures the probability of observing results as extreme as the ones obtained, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, suggesting that the observed effect is statistically significant.”

2. How would you assess the performance of a predictive model?

This question evaluates your knowledge of model evaluation metrics and their application.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and when to use each.

Example

“I assess model performance using metrics like accuracy for balanced datasets, while precision and recall are more relevant for imbalanced classes. I also use ROC-AUC to evaluate the trade-off between true positive and false positive rates.”

3. Can you describe a time when you used statistical analysis to solve a business problem?

This question allows you to showcase your analytical skills in a real-world context.

How to Answer

Provide a specific example, detailing the problem, your analysis, and the impact of your findings.

Example

“I analyzed customer feedback data to identify key drivers of satisfaction. By applying regression analysis, I found that response time significantly impacted satisfaction scores, leading to process improvements that increased customer satisfaction by 15%.”

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

Understanding fundamental statistical principles is essential for data analysis.

How to Answer

Explain the 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 important because it allows us to make inferences about population parameters using sample statistics.”

Data Engineering

1. How do you approach data cleaning and preprocessing?

Data quality is critical in analytics, and your approach to cleaning data will be scrutinized.

How to Answer

Discuss the steps you take to clean and preprocess data, including handling missing values and outliers.

Example

“I start by assessing the dataset for missing values and outliers. I use imputation techniques for missing data and apply transformations to handle outliers. This ensures the dataset is clean and ready for analysis, which is crucial for accurate modeling.”

2. Describe your experience with SQL and how you use it in data analysis.

SQL skills are essential for querying databases and extracting insights.

How to Answer

Highlight your experience with SQL, including specific functions and queries you commonly use.

Example

“I have extensive experience using SQL for data extraction and manipulation. I frequently use JOINs to combine datasets, aggregate functions to summarize data, and window functions for advanced analytics, which allows me to derive meaningful insights efficiently.”

3. What strategies do you use to optimize database performance?

This question assesses your understanding of database management and optimization techniques.

How to Answer

Discuss indexing, query optimization, and data partitioning as strategies to enhance performance.

Example

“To optimize database performance, I implement indexing on frequently queried columns, analyze query execution plans to identify bottlenecks, and use data partitioning to improve query response times, ensuring efficient data retrieval.”

4. How do you ensure data integrity and accuracy in your analyses?

Data integrity is crucial for reliable insights, and your approach to maintaining it will be evaluated.

How to Answer

Explain the measures you take to validate and verify data accuracy throughout the analysis process.

Example

“I ensure data integrity by implementing validation checks during data entry, conducting regular audits, and cross-referencing data with trusted sources. This rigorous approach helps maintain high data quality, which is essential for accurate analysis.”