As a Data Scientist at Included Health, you will occupy a highly strategic role at the intersection of healthcare, technology, and machine learning. Included Health is dedicated to raising the standard of healthcare for everyone, and data is the primary engine driving this mission. In this role, you will build and deploy models that solve complex clinical and operational challenges, such as predicting patient health risks, optimizing provider recommendation engines, and matching members to the most appropriate clinical care pathways.

The impact of your work is direct and profound. Rather than optimizing for clicks or ad revenue, your algorithms will influence real-world clinical outcomes, guide patients through a fragmented healthcare system, and lower care costs for millions of members. This means you will deal with massive, highly disparate, and often unstructured healthcare datasets, requiring a high degree of technical ingenuity, statistical rigor, and domain empathy.

To succeed, you must be more than just a model builder; you must be an exceptional communicator who can translate complex statistical concepts into actionable product strategies. The team operates in a fast-paced environment where collaboration with product managers, clinical experts, and software engineers is a daily necessity. If you are motivated by high-stakes problem-solving and want to apply cutting-edge data science to human-centric problems, this role offers an incredibly rewarding platform.

The following questions are representative of what you can expect to encounter throughout the hiring process. They are drawn from real interview experiences at Included Health and are designed to test your technical foundations, product intuition, and communication style.

Machine Learning Breadth & Depth

These questions evaluate your fundamental understanding of statistical modeling, machine learning algorithms, and your ability to defend your design choices.

• Explain the trade-offs between a random forest model and a gradient-boosted decision tree (GBDT) for predicting patient engagement.
• How do you handle highly imbalanced datasets when training a model to detect rare clinical conditions?

Preparing for an interview at Included Health requires a balanced strategy that addresses both technical expertise and communication skills. The interviewers want to see how you think, how you handle messy data, and how you collaborate.

Technical Rigor – You must demonstrate a strong grasp of machine learning fundamentals and coding. Brush up on core statistical concepts, classic ML algorithms, and SQL/Python basics. You should be able to write clean, efficient code and explain the mathematical trade-offs of your modeling decisions.

Structured Problem Solving – When faced with ambiguous case studies, avoid jumping straight to a modeling solution. Instead, ask clarifying questions, structure your approach systematically, and explain your assumptions clearly. Interviewers value candidates who can break down a massive healthcare problem into digestible analytical steps.

Communication & Influence – Data science at Included Health is highly collaborative. You must be able to translate complex data insights into clear product recommendations. Practice explaining highly technical projects in simple, impactful terms, focusing on the business and clinical "why" behind your technical choices.

Mission Alignment – Healthcare is deeply personal. Show that you are genuinely interested in the company's mission to improve lives. Research Included Health's products and challenges, and be ready to discuss how your skills can help solve them.

The interview process at Included Health is designed to evaluate your technical depth, product intuition, and cultural alignment. It is a structured journey that progresses from high-level screens to a comprehensive, multi-stage final round.

Your candidate journey begins with a standard recruiter screen, followed by a technical screen or a conversation with the hiring manager. This stage usually involves a mix of coding, machine learning basics, and an in-depth review of your past project experience. Some pipelines may also include an open-ended take-home data challenge where you are asked to analyze disparate datasets and present your findings.

The final round is highly structured and rigorous, typically consisting of four distinct interviews. These panels cover product case studies, machine learning depth, machine learning breadth, and leadership. Throughout this loop, the team places a strong emphasis on your ability to communicate your ideas clearly, rather than just focusing on raw statistical performance.

The timeline shown above outlines the typical progression from your initial contact to the final offer. While the pace can vary depending on the team's urgency, you should expect the entire process to take between three to six weeks. Use this timeline to pace your preparation, ensuring you allocate sufficient time for coding practice before the technical screen and system design/case study practice before the final loop.

To pass the Included Health interview loop, you must perform consistently across several core competencies. Understanding what interviewers look for in each area will help you focus your preparation effectively.

Machine Learning Depth & Breadth

This evaluation area tests your theoretical knowledge and practical experience in building machine learning systems. Interviewers want to ensure you don't just treat models as "black boxes" but deeply understand how they work, when they fail, and how to scale them.

Be ready to go over:

• Model Selection & Trade-offs – Understanding when to use simple linear models versus complex ensemble methods or deep learning.
• Feature Engineering – Handling missing data, high-cardinality categorical variables, and temporal features common in healthcare.
• Validation Strategies – Designing robust validation schemes (e.g., time-series split, stratified k-fold) to prevent data leakage.
• Advanced concepts (less common) – Natural Language Processing (NLP) for clinical notes, survival analysis for patient retention, and causal inference.

Example questions or scenarios:

• "How would you design a model to predict whether a member will show up for their scheduled virtual clinic appointment?"
• "Explain the difference between L1 and L2 regularization and how they affect feature selection in a high-dimensional clinical dataset."

Product Case Studies & Applied Analytics

This round evaluates your product intuition and your ability to use data to solve ambiguous business problems. You will be presented with a high-level healthcare scenario and asked to design a data-driven solution.

Be ready to go over:

• Metric Definition – Defining clear, actionable North Star metrics and guardrail metrics for a new product feature.
• Experimentation – Designing A/B tests, calculating sample sizes, and handling non-standard experimentation setups.
• Data Wrangling Strategy – Explaining how you would process, clean, and join disparate, messy datasets to extract meaningful features.

Example questions or scenarios:

• "We want to launch a new feature that matches members with mental health specialists. How would you design the matching algorithm, and how would you measure its success?"
• "You are given three different datasets containing claims, app logs, and patient surveys. How do you combine them to identify friction points in the member journey?"

Coding & Technical Screen

The coding round assesses your ability to write clean, production-ready code. While the problems are rarely as abstract as pure competitive programming, they require solid algorithm and data structure fundamentals.

Be ready to go over:

• SQL Proficiency – Writing complex queries involving joins, window functions, and aggregations to analyze user behavior.
• Python Scripting – Implementing algorithmic logic, manipulating data structures, and optimizing code performance.
• Code Quality – Writing readable, modular code with appropriate variable naming and edge-case handling.

Example questions or scenarios:

• "Write a function to merge overlapping intervals representing patient stay durations in a hospital."
• "Given a database of clinical visits, write a SQL query to calculate the rolling 30-day readmission rate for each hospital."

Leadership & Communication

This round focuses on your soft skills, collaboration style, and alignment with the values of Included Health. The team wants to ensure you can work effectively with cross-functional partners and handle conflict constructively.

Be ready to go over:

• Cross-functional Collaboration – Working with product managers, engineers, and clinical experts to deliver data products.
• Project Ownership – Taking a project from an ambiguous idea to a deployed, high-impact solution.
• Handling Ambiguity – Navigating shifting priorities, incomplete data, and tight deadlines.

Example questions or scenarios:

• "Tell me about a time you had to deliver a data science project with incomplete or highly corrupted data. How did you manage expectations?"
• "Describe a situation where your technical recommendation was challenged by a business stakeholder. How did you build consensus?"