As a Data Scientist at Humana, you will play a pivotal role in transforming healthcare delivery through data-driven decision-making. Humana is one of the nation's largest healthcare insurers, and its data science teams are tasked with solving some of the most complex challenges in the industry. Your work will directly impact millions of members by optimizing clinical outcomes, improving patient experiences, and streamlining operational efficiencies.

In this role, you will work within a highly collaborative environment, partnering with clinical experts, product managers, software engineers, and business leaders. The models you build will help predict health risks, prevent chronic condition progression, and ensure that members receive the right care at the right time. Whether you are optimizing provider networks or developing predictive models to identify fraud, your contributions will have a tangible, positive impact on human lives.

This position requires a unique blend of deep technical expertise, business acumen, and a passion for healthcare. You will navigate massive, complex datasets, utilizing advanced machine learning algorithms, statistical modeling, and data visualization tools to translate raw data into actionable clinical and business strategies.

To help you prepare effectively, we have compiled a list of common interview questions based on real candidate experiences at Humana. These questions span technical coding, machine learning theory, project walkthroughs, and behavioral alignment.

Coding and SQL Challenges

These questions assess your foundational programming skills in Python and your ability to manipulate complex relational databases.

• Write a Python function to solve a classic algorithmic problem, such as finding the first non-repeating character in a string or reversing a linked list.
• Given a table of member transactions, write a SQL query using window functions to rank members by their total healthcare expenditure within each region.

Preparing for an interview at Humana requires a balanced approach that demonstrates both technical rigor and a strong alignment with the company's healthcare mission. You should approach your preparation with a structured plan, focusing on core competencies that the hiring teams value most.

Technical Rigor – You must demonstrate a strong foundation in Python, SQL, and machine learning theory. Be ready to explain the mathematical concepts behind your models and write clean, efficient code under time constraints.

Analytical Problem-Solving – Interviewers will evaluate how you approach ambiguous business and clinical problems. Focus on structuring your thoughts logically, asking clarifying questions, and breaking down complex challenges into manageable components.

Cross-Functional Communication – As a Data Scientist, you must be able to bridge the gap between technical complexity and business strategy. Practice explaining advanced machine learning concepts in simple terms and highlighting the business value of your technical work.

Healthcare Mission Alignment – Humana is deeply mission-driven. You should be prepared to discuss why you want to apply your data science skills to healthcare and how you plan to contribute to improving member health outcomes.

The interview process for a Data Scientist at Humana is structured to evaluate your technical capabilities, problem-solving skills, and cultural fit. While the exact steps can vary slightly depending on the specific team and seniority level, the overall flow remains consistent and thorough.

You will typically begin with an automated screening phase, which often includes an AI-driven chatbot questionnaire or a self-recorded video interview where you answer behavioral and foundational questions. This is followed by a conversational round with the hiring manager to discuss your background and align expectations. The core technical evaluation consists of live coding, SQL assessments, and deep dives into machine learning theory. Finally, you will meet with senior leadership and potential team members to assess your project experience and cultural fit.

The visual timeline above outlines the typical progression a candidate goes through during the hiring journey. You should use this timeline to pace your preparation, ensuring you allocate sufficient time to practice coding and SQL before the technical screens, while saving energy for behavioral and leadership rounds at the end. Note that some teams may also include a take-home technical assignment between the initial screen and the live technical panel.

To succeed in the Humana interview process, you must understand the specific areas where you will be evaluated. Hiring managers look for candidates who possess a robust technical toolkit combined with practical business application skills.

Live Coding and SQL Capabilities

The technical screen is designed to test your hands-on coding and data manipulation skills. You will be asked to solve algorithmic problems in Python and write SQL queries to extract and analyze data.

Be ready to go over:

• SQL Window Functions – Mastering functions like ROW_NUMBER(), RANK(), and LEAD/LAG is critical, as SQL screens often focus on ranking and sequential data analysis.
• Python Data Structures – Understanding when to use lists, dictionaries, sets, and tuples to write efficient code.
• Data Cleaning and Aggregation – Demonstrating your ability to handle missing values, outliers, and complex joins in SQL and pandas.
• Coding in Simple Environments – Being prepared to write code without the aid of advanced IDEs or auto-complete tools.

Example scenarios:

• "Write a SQL query to find the top three highest-spending members in each state, handling ties appropriately."
• "Write a Python function that takes a list of patient appointment times and merges any overlapping slots."
• "Explain how you would write a script to parse and clean a raw, unstructured log file containing system errors."

Machine Learning Theory and Use Cases

Hiring teams want to ensure you understand the "why" behind the models you build, rather than just importing libraries and running algorithms blindly.

Be ready to go over:

• Supervised Learning Algorithms – Deep understanding of linear regression, logistic regression, decision trees, random forests, and gradient boosting.
• Model Evaluation and Validation – Choosing the right metrics for clinical models, implementing cross-validation, and preventing overfitting.
• Feature Engineering – Techniques for transforming raw healthcare data into meaningful model inputs.
• Advanced concepts (less common) – Natural Language Processing (NLP) for analyzing clinical notes, deep learning for medical imaging, and survival analysis for patient retention.

Example scenarios:

• "How would you design an early warning system to predict which patients are at high risk of developing diabetes?"
• "Walk me through the mathematical difference between L1 (Lasso) and L2 (Ridge) regularization."
• "How would you explain the concept of feature importance in a random forest model to a medical director?"

Data Visualization and Dashboarding

Humana highly values the ability to present data in a visually compelling and easily digestible format. Candidates are frequently evaluated on their experience with business intelligence tools.

Be ready to go over:

• Power BI Proficiency – Creating interactive dashboards, writing DAX queries, and designing user-friendly layouts.
• Storytelling with Data – Structuring a visual narrative that guides stakeholders from high-level KPIs down to root-cause analyses.
• Dashboard Optimization – Ensuring fast load times and seamless user interactions when working with large underlying datasets.

Example scenarios:

• "Describe a dashboard you built in the past. What were the key metrics, and how did the business use it to make decisions?"
• "How would you handle a situation where a business stakeholder requests a dashboard that contains too many cluttered visual elements?"

As a Data Scientist at Humana, your daily work will be dynamic, collaborative, and highly focused on delivering measurable business and clinical value. You will be responsible for the entire data science lifecycle, from data acquisition and preprocessing to model development, deployment, and monitoring.

You will collaborate closely with data engineers to build robust data pipelines, ensuring your models have access to high-quality, clean data. You will also partner with product managers and clinical leaders to understand their business requirements and translate them into technical solutions. A significant portion of your role will involve communicating your findings, which includes building interactive Power BI dashboards and presenting model insights to executive leadership.

Additionally, you will participate in standard Agile methodologies, including sprint planning, retrospectives, and daily stand-ups. Your goal will be to continuously iterate on your models, ensuring they remain accurate, scalable, and aligned with Humana's evolving business needs.