What is a Data Scientist at Change Healthcare?

As a Data Scientist at Change Healthcare, you are stepping into a pivotal role within a massive B2B technology ecosystem. Change Healthcare specializes in clinical claims and revenue cycle management, acting as the connective tissue for healthcare systems across the United States. In this role, your work directly impacts how efficiently hospitals operate, how accurately claims are processed, and how healthcare data can be leveraged to improve patient and financial outcomes.

What makes this position uniquely compelling is the company’s dedicated investment in artificial intelligence. Unlike many healthcare tech firms, Change Healthcare boasts a Chief AI Officer, signaling a top-down commitment to advanced analytics. Furthermore, the organizational structure clearly separates Data Science from Machine Learning Engineering (MLE) and Software Development Engineering (SDE). This means your primary mandate as a Data Scientist is innovation, prototyping, and complex problem-solving, rather than getting bogged down in deployment pipelines.

As part of the OptumInsight family (under UnitedHealth Group), the scale of data you will work with is staggering. You will be expected to navigate vast, complex healthcare datasets to uncover actionable insights. If you thrive on building innovative models and shaping the strategic direction of healthcare technology, this role offers an unparalleled platform for impact.

Common Interview Questions

The following questions represent the types of challenges candidates frequently face. While you should not memorize answers, you should use these to practice structuring your thoughts, especially under pressure.

Technical and Statistical Foundations

Interviewers use these questions to ensure your mathematical intuition is as strong as your coding ability.

• How do you handle missing values in a dataset where the missingness is not random?
• Explain the bias-variance tradeoff and how it applies to a model you recently built.
• What is the difference between L1 and L2 regularization? When would you use each?
• Write a SQL query to find the top 5 hospitals with the highest claim denial rates over the last quarter.
• How would you evaluate a clustering algorithm if you do not have ground truth labels?

B2B Healthcare Problem Solving

These questions test your ability to apply data science to Change Healthcare’s specific business model.

• How would you design a model to predict the likelihood of a patient paying their out-of-pocket medical bills?
• If you are given a dataset of millions of clinical claims, how would you go about identifying fraudulent billing patterns?
• What metrics would you use to prove to a hospital administrator that our predictive model is saving them money?
• How do you deal with data drift when healthcare billing codes are updated annually?

Behavioral and Pressure-Testing

Expect direct, sometimes confrontational questions designed to test your confidence and cultural fit.

• Why should we hire you over the other candidates the recruiter sourced?
• Tell me about a time someone criticized your technical approach. How did you respond?
• Describe a time you had to explain a complex machine learning concept to a non-technical stakeholder.
• How do you ensure a smooth handoff of your models to the Machine Learning Engineering team?

Getting Ready for Your Interviews

Preparation is about more than just brushing up on algorithms; it requires aligning your technical expertise with the specific business challenges of healthcare claims and B2B solutions. You should approach your preparation by focusing on the following core evaluation criteria:

Problem-Solving and Innovation – Because the Data Science team focuses heavily on innovation, interviewers want to see how you tackle ambiguous problems. You can demonstrate strength here by clearly structuring your thoughts, asking clarifying questions, and proposing creative but mathematically sound approaches to open-ended healthcare scenarios.

Technical and Statistical Foundations – This evaluates your grasp of the underlying mechanics of machine learning and statistics. Interviewers will look for your ability to explain why you chose a specific model, how you handle imbalanced healthcare data, and your intuition for feature engineering in a clinical context.

Cross-Functional Collaboration – Since you will be handing off models to dedicated MLE and SDE teams, your ability to communicate complex data concepts to engineering partners is critical. You must show that you can write clean prototype code and document your methodologies clearly.

Resilience and Confidence – Interviews here can sometimes feature direct, high-pressure questioning. Interviewers evaluate your ability to remain composed, defend your technical choices logically, and articulate your unique value proposition without becoming defensive.

Interview Process Overview

The interview process for a Data Scientist at Change Healthcare is thorough and can sometimes extend over a longer timeline than typical tech interviews. You will generally start with one or more HR screening calls to establish your background, salary expectations, and basic technical alignment. Because the process is rigorous, do not be surprised if there are multiple phone screens before you advance to the next stage.

Following the HR screens, you will typically face a technical and behavioral screen with the Hiring Manager. This conversation is often conducted via Microsoft Teams and can be quite intense. Hiring managers are known to pressure-test candidates right out of the gate, asking pointed questions about your background and why you stand out among other applicants. If you pass this hurdle, you will move to the virtual onsite loop, which consists of several rounds focusing on technical depth, case studies, and behavioral fit with various team members.

This timeline illustrates the typical progression from initial recruiter contact through the intensive hiring manager screen and the multi-round onsite loop. You should use this visual to pace your preparation, ensuring your foundational technical skills are sharp for the early screens while reserving deep dive case-study practice for the onsite stages. Note that timelines can occasionally stretch, so patience and consistent follow-up are key.

Deep Dive into Evaluation Areas

To succeed in the onsite loop, you must be prepared to demonstrate depth across several specific domains. Change Healthcare interviewers look for a blend of rigorous statistical knowledge and practical business acumen.

Machine Learning & Statistical Foundations

This area tests your core competency as a Data Scientist. Because you are tasked with innovation, you must understand the math behind the models, not just how to implement them via libraries. Interviewers want to see that you can select the right tool for the job and deeply understand model trade-offs.

Be ready to go over:

• Supervised vs. Unsupervised Learning – Knowing when to apply clustering for patient segmentation versus predictive modeling for claim denials.
• Handling Imbalanced Data – Essential for healthcare, where anomalies (like fraud or rare diseases) make up a tiny fraction of the dataset.
• Model Evaluation Metrics – Precision, recall, F1-score, and ROC-AUC, specifically contextualized to the cost of false positives vs. false negatives in healthcare.
• Advanced concepts (less common) – Deep learning for NLP (extracting insights from unstructured clinical notes), graph neural networks for provider networks, and survival analysis.

Example questions or scenarios:

• "Explain how you would handle a dataset where only 1% of the claims are fraudulent."
• "Walk me through the mathematical difference between Random Forest and Gradient Boosting. When would you choose one over the other?"
• "How do you ensure your model does not overfit when dealing with highly dimensional clinical data?"

Healthcare B2B Problem Solving & Case Studies

Change Healthcare operates in the complex world of clinical claims and revenue cycle management. You are evaluated on your ability to translate ambiguous business problems into structured data science projects. Strong performance means you ask the right questions about the end-user before you start talking about algorithms.

Be ready to go over:

• Revenue Cycle Management (RCM) – Understanding the lifecycle of a healthcare claim from patient encounter to final payment.
• Data Nuances – Dealing with missing, messy, or non-standardized data from different hospital systems.
• Feature Engineering – Transforming raw transactional data into meaningful predictors.
• Advanced concepts (less common) – Optimizing human-in-the-loop systems where AI flags claims for manual review by billing specialists.

Example questions or scenarios:

• "We want to predict which healthcare claims will be denied by insurance providers. How would you frame this machine learning problem?"
• "If a hospital client says our model's predictions are suddenly inaccurate, how would you troubleshoot the issue?"
• "Design a system to segment healthcare providers based on their billing behaviors."

Behavioral and Cross-Functional Fit

Because Change Healthcare has distinct Data Science and Machine Learning Engineering teams, collaboration is vital. You will be evaluated on your ability to work alongside MLEs who productionize your work, as well as your ability to handle direct feedback and high-pressure situations.

Be ready to go over:

• Conflict Resolution – Navigating disagreements on technical approaches with engineering counterparts.
• Communication of Complexity – Explaining complex AI concepts to non-technical stakeholders or business leaders.
• Handling Pressure – Maintaining composure when your background or approach is aggressively questioned.
• Advanced concepts (less common) – Influencing product roadmaps based on exploratory data findings.

Example questions or scenarios:

• "Tell me about a time you developed a prototype that an engineering team struggled to put into production. How did you resolve it?"
• "Why did the recruiter contact you for this role instead of the dozens of other candidates in our pipeline?"
• "Describe a situation where you had to pivot your entire modeling approach because the initial data was flawed."

Key Responsibilities

As a Data Scientist at Change Healthcare, your day-to-day work is heavily skewed toward research, prototyping, and exploratory analysis. You will spend a significant portion of your time querying massive databases of clinical claims and financial records to identify patterns that can save healthcare systems money or reduce administrative friction.

You will work closely with product managers to understand the pain points of B2B clients. Once a problem is defined, you will build and validate predictive models, focusing on accuracy, interpretability, and business value. Because the company separates its AI disciplines, you will not be solely responsible for building scalable APIs or maintaining cloud infrastructure. Instead, your deliverable is often a highly tuned, validated model and a clear set of documentation that you will hand off to the MLE team in the Bay Area for deployment.

Your role also involves acting as an internal consultant. You will frequently present your findings to leadership, including the Chief AI Officer's organization, advocating for new approaches or technologies that can keep Change Healthcare at the forefront of healthcare innovation.

Role Requirements & Qualifications

To be competitive for this position, you need a strong mix of technical prowess and domain adaptability.

• Must-have technical skills – Advanced proficiency in Python (Pandas, Scikit-learn, TensorFlow/PyTorch) and SQL. You must be highly capable of extracting and manipulating massive datasets.
• Must-have experience – A solid academic foundation (often a Master’s or Ph.D. in a quantitative field) paired with proven industry experience building end-to-end machine learning prototypes.
• Nice-to-have domain knowledge – Prior experience with healthcare data, specifically clinical claims, EHR/EMR systems, or revenue cycle management (RCM) is a massive differentiator.
• Nice-to-have technical skills – Experience with Natural Language Processing (NLP) for unstructured clinical text, and familiarity with the hand-off process to MLE teams using tools like MLflow, Docker, or Git.
• Soft skills – Exceptional resilience, the ability to thrive under direct questioning, and strong stakeholder management skills.

Frequently Asked Questions

Q: How long does the interview process typically take? The process at Change Healthcare is known to be lengthy. It is common to experience multiple phone screens before an onsite is scheduled, and the end-to-end process can easily take over a month. Patience and proactive communication with your recruiter are essential.

Q: Is the Data Science team remote or office-based? Historically, the core Data Science (AI) team was centralized in Seattle, WA, while the MLE team was based in the Bay Area. Following the acquisition by OptumInsight, hybrid and remote flexibilities have evolved, but you should expect to collaborate heavily with Pacific Time Zone hours.

Q: What is the culture like during the interview? Experiences vary by hiring manager. While many find the process pleasant and intellectually stimulating, some hiring managers employ aggressive, pressure-testing tactics during screens. Prepare to confidently defend your background and remain unfazed by blunt questioning.

Q: How much healthcare domain knowledge is required? While you do not need to be a medical expert, understanding the basics of how healthcare providers bill insurance companies (Revenue Cycle Management) will give you a massive advantage in case study rounds.

Other General Tips

• Lean into the DS vs. MLE Distinction: Change Healthcare explicitly separates these roles. Emphasize your passion for exploratory data analysis, algorithm design, and innovation, rather than spending all your time talking about CI/CD pipelines and infrastructure.
• Hold Your Ground Professionally: If challenged on your background or the validity of your methods, do not panic or back down immediately. Calmly explain your rationale. Interviewers want to see that you can stand behind your work when challenged by stakeholders.

• Brush up on Claims Data Nuances: Healthcare data is notoriously messy. Mentioning techniques for handling sparse matrices, non-standardized text, or class imbalances will show you are ready for the reality of the job.

• Structure Your Case Answers: When given an open-ended scenario, always start by defining the business objective, then move to data availability, feature engineering, model selection, and finally, evaluation metrics.

Summary & Next Steps

Securing a Data Scientist position at Change Healthcare is an opportunity to work at the intersection of advanced artificial intelligence and massive-scale healthcare data. Because the company values innovation and has a dedicated AI leadership structure, you will be empowered to solve high-impact problems that directly improve the efficiency of the U.S. healthcare system.

This compensation data provides a baseline for what you can expect at this level within the organization. Use these figures to set realistic expectations and negotiate confidently when discussing compensation with your recruiter, keeping in mind that the integration with OptumInsight may influence total rewards packages.

To succeed, you must demonstrate a deep understanding of machine learning fundamentals, a pragmatic approach to messy healthcare data, and the resilience to navigate a rigorous, sometimes high-pressure interview process. Focus your preparation on articulating your problem-solving frameworks and proving you can collaborate effectively with engineering teams to bring your innovations to life. Continue to refine your technical narrative, practice your case studies, and leverage resources on Dataford to ensure you are fully prepared. You have the analytical foundation necessary for this role—now it is time to show them your strategic vision.