Included Health Data Scientist interview questions & guide 2026

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.

As a Data Scientist at Included Health, your daily responsibilities will span the entire lifecycle of data product development, from initial exploration to production deployment.

• Model Development & Deployment – You will design, train, and deploy machine learning models that power core product features, such as clinical navigation engines, personalized messaging systems, and provider quality scoring.
• Cross-Functional Collaboration – You will work closely with Product Managers to define product roadmaps, Software Engineers to integrate models into production pipelines, and Clinical Leads to ensure algorithmic decisions align with medical best practices.
• Data Exploration & Pipeline Building – You will analyze disparate, complex healthcare datasets (including claims data, electronic health records, and user engagement logs) to uncover insights and build robust feature pipelines.
• Experimentation & Evaluation – You will design and analyze offline evaluations and online A/B tests to measure the impact of new models and product features on clinical and business outcomes.
• Stakeholder Communication – You will translate complex statistical findings and model behaviors into clear, compelling narratives for both technical and non-technical audiences, influencing strategic company decisions.

To be competitive for the Data Scientist position at Included Health, you must demonstrate a strong blend of technical expertise, practical experience, and soft skills.

• Must-have skills – Strong proficiency in Python and SQL. Solid understanding of machine learning algorithms, statistical modeling, and experimental design. Experience working with large, messy datasets and translating them into actionable business insights.
• Nice-to-have skills – Experience with cloud platforms (e.g., AWS, GCP) and big data technologies (e.g., Spark, Snowflake). Prior experience in the healthcare technology domain or working with clinical/claims data. Experience deploying models to production environments.
• Experience level – Typically requires a Master's or PhD in a quantitative field (e.g., Computer Science, Statistics, Engineering, Economics) or equivalent practical experience, along with 3+ years of professional experience as a practicing data scientist.
• Soft skills – Exceptional communication and storytelling skills. Ability to thrive in an ambiguous, fast-paced environment. Strong collaborative mindset and a passion for improving healthcare outcomes.

Q: How technical is the coding screen for this role? A: The coding screen is practical and focused on data manipulation and basic algorithms. You should expect SQL questions involving aggregations and window functions, alongside Python tasks that test your ability to write clean, modular code to solve logical problems. It is less about competitive programming and more about production readiness.

Q: What is the company's policy on remote work for Data Scientists? A: Included Health supports a highly flexible, hybrid, and remote-friendly work environment. While some teams prefer proximity to major hubs like San Francisco, many data science positions are open to fully remote candidates within the United States.

Q: How much domain knowledge of healthcare is required? A: While prior experience with healthcare data (such as claims or clinical codes) is a significant plus, it is not a strict prerequisite. The interviewers place a higher premium on your core data science skills, structured problem-solving ability, and your willingness to learn the complexities of the healthcare domain.

Q: What sets apart successful candidates in the product case study round? A: Successful candidates excel at structuring ambiguous problems. They do not immediately jump into complex modeling. Instead, they start by defining the business goal, identifying the target user, discussing data limitations, and outlining how they would measure success before proposing technical solutions.

Q: How long does the interview process take from start to finish? A: The typical timeline from the initial recruiter call to a final decision is three to six weeks. The team is generally transparent about timelines, but candidates are encouraged to proactively follow up with their recruiter after major rounds.

To maximize your chances of success, keep these practical, insider tips in mind as you navigate the Included Health interview loop:

• Focus on the "Why" Over the "What" – When presenting past projects, don't just explain the architecture of your model. Clearly articulate why you chose that approach, what business or clinical problem it solved, and the measurable impact it delivered.
• Prioritize Communication in Case Studies – During open-ended data questions, the interviewers are evaluating your communication style and structured thinking. Focus on explaining your ideas clearly and logically, rather than trying to impress them with complex statistical jargon.
• Clarify Recruiter Expectations Early – The recruitment process can sometimes feel fast-moving or ambiguous. Do not hesitate to ask your recruiter for explicit details on what each round will cover, the technologies expected, and who you will be meeting with.

• Align with the Mission – Included Health is a mission-driven company. Take time to understand their products and target demographics. Infuse your answers with a genuine interest in solving hard healthcare challenges and improving patient lives.

Securing a Data Scientist role at Included Health is an exciting opportunity to apply your technical skills to some of the most meaningful challenges in healthcare. The interview process is rigorous but fair, designed to evaluate not only your technical brilliance but also your product intuition, structured thinking, and communication skills. By preparing thoroughly across machine learning fundamentals, coding, and open-ended case studies, you can set yourself apart as a highly competitive candidate.

As you prepare, focus on building structured frameworks for solving ambiguous data problems and practice translating complex technical concepts into clear, impactful stories. For more detailed interview insights, company-specific preparation resources, and real candidate experiences, be sure to explore the tools available on Dataford.

The salary data shown above represents the typical compensation range for a Data Scientist at Included Health. When reviewing this data, keep in mind that total compensation often includes a base salary, performance bonuses, and equity options. Your specific offer will depend on your experience level, technical depth, and geographic location. Use these insights to guide your expectations and navigate your offer negotiations with confidence.