Be ready to go over:

• Claims Data Structures – Understanding the differences between institutional claims, professional claims, and pharmacy claims.
• Coding Systems – Familiarity with standard medical terminologies such as ICD-10 diagnosis codes, CPT/HCPCS procedure codes, and NDC pharmacy codes.
• Enrollment & Eligibility – How to calculate member-months, handle retro-active eligibility changes, and account for missing data in longitudinal analyses.
• Advanced concepts (less common) – Risk adjustment methodologies (e.g., CMS-HCC, Milliman Advanced Risk Adjusters), healthcare quality metrics, and actuarial cost-trend forecasting.

Example scenarios:

• "How would you clean and aggregate a raw, multi-million row claims database to identify the primary drivers of inpatient spend inflation over a three-year period?"
• "Describe how you would account for lag time in claims processing (IBNR - Incurred But Not Reported) when building a real-time utilization model."

R Programming, Shiny, & Tooling

Many data science teams at Milliman rely heavily on R as their primary language for modeling, package development, and client delivery. Your programming mechanics, code organization, and dashboard development skills will be thoroughly tested.

Be ready to go over:

• R Package Development – How to write modular, documented, and unit-tested code within custom R packages to ensure reproducibility across consulting teams.
• Shiny Application Architecture – Designing reactive frameworks, managing user sessions efficiently, and optimizing UI layouts for client presentations.
• Data Wrangling & Performance – Utilizing performance-oriented libraries like data.table or tidyverse to process large datasets in memory.
• Advanced concepts (less common) – Integrating C++ code via Rcpp, calling external REST APIs within R, and deploying Shiny apps to enterprise servers.

Example scenarios:

• "Walk us through how you would optimize a Shiny dashboard that is lagging because it is pulling and rendering millions of rows of data dynamically based on user inputs."
• "Explain your approach to implementing secure user authentication and role-based data filtering within a client-facing web application."

Case Study & Technical Presentation

In this stage, you are evaluated on your ability to act as a consultant. You will typically be given a business problem and a dataset, and you must present your methodology, modeling choices, and business recommendations to a panel.

Be ready to go over:

• Problem Structuring – How you translate a vague business prompt into a concrete data science objective.
• Feature Engineering & Selection – Defending why you chose specific variables and how you handled multicollinearity or high-cardinality categorical features.
• Business Translation – Explaining model metrics (like AUC, RMSE, or precision-recall) in terms of financial risk, cost savings, or operational efficiency for the client.
• Advanced concepts (less common) – Handling highly imbalanced datasets in risk prediction, model interpretability frameworks (SHAP, LIME), and model deployment strategy.

Example scenarios:

• "Present a slide deck detailing how you would design a predictive model to identify health insurance policyholders at high risk of high-cost chronic disease complications next year."
• "During your presentation, an actuary challenges your choice of a random forest model over a simple logistic regression. How do you defend your choice while validating their concerns?"

As a Data Scientist at Milliman, your daily work will span the entire lifecycle of data analysis and client delivery. You will operate as both a technical builder and an internal consultant, ensuring that your analytical solutions address real-world business challenges.

Your primary responsibilities will include:

• Developing Predictive Models – Designing, training, and validating statistical and machine learning models to forecast healthcare costs, predict patient outcomes, evaluate insurance risks, or optimize financial portfolios.
• Building Interactive Client Tools – Creating and maintaining professional, production-grade interactive applications (primarily using R Shiny) that allow non-technical clients to explore data, run scenarios, and visualize risk metrics.
• Collaborating with Actuaries & Consultants – Working closely with multidisciplinary teams to integrate data science methodologies into traditional actuarial workflows, combining the best of statistical learning with deep domain expertise.
• Handling Large-Scale Data Pipelines – Ingesting, cleaning, and transforming massive, complex datasets, such as medical claims, electronic health records (EHR), or financial transactions, into structured formats suitable for modeling.
• Communicating Insights – Writing clear technical documentation, preparing client-facing slide decks, and presenting analytical findings directly to stakeholders, explaining both the "how" and the "why" of your models.

Because Milliman's reputation is built on precision and deep expertise, the qualifications for Data Scientist roles are rigorous. Candidates must display a strong balance of programming mechanics, statistical theory, and industry business acumen.

• Must-Have Technical Skills – Deep proficiency in R (including package development and data manipulation libraries) or Python, along with strong SQL skills for querying large databases.
• Must-Have Domain Experience – Demonstrated experience working with large-scale, complex datasets. For healthcare-focused teams, prior experience with medical claims, pharmacy claims, or electronic health records is highly critical.
• Nice-to-Have Skills – Experience with R Shiny for dashboard development, SAS for legacy code migration, API integration, and cloud-based data warehouses (e.g., Snowflake, AWS, Azure).
• Education & Experience – Typically a Master’s or Ph.D. in a quantitative field (such as Statistics, Biostatistics, Data Science, Computer Science, or Actuarial Science) and 2+ years of professional experience, or a Bachelor's degree with equivalent highly relevant industry experience.
• Soft Skills – Outstanding verbal and written communication skills, a consultative mindset, the ability to work independently in a decentralized environment, and strong critical thinking.

Q: How technically difficult are the interviews at Milliman? A: The technical difficulty is generally average to difficult, but it depends heavily on your alignment with the team's domain. If you have a strong background in R and healthcare claims, the technical questions will feel highly intuitive. If you lack healthcare or insurance experience, the domain-specific questions can make the process feel significantly more challenging.

Q: How much prep time should I budget for the interview process? A: You should budget at least 2 to 3 weeks of focused preparation. This allows you enough time to complete the multi-hour technical assessment, refine your portfolio of R or Shiny projects, and thoroughly research healthcare claims structures and actuarial risk concepts.

Q: What is the company culture like for Data Scientists? A: Milliman operates with a highly professional, academic, and decentralized culture. There is a strong emphasis on intellectual curiosity, precision, and high-quality deliverables. Because teams operate semi-autonomously, you will enjoy a high degree of ownership over your work, but you must be comfortable navigating ambiguity and driving projects forward independently.

Q: How quickly does the interview process move from initial screen to offer? A: The timeline can vary from 3 to 6 weeks. Because different offices and practice groups manage their own hiring pipelines, some processes move incredibly fast, while others may take longer due to coordinating panel schedules with busy consulting actuaries.

To maximize your chances of securing an offer at Milliman, keep these practical, insider tips in mind during your preparation and interview stages.

• Highlight claims data early: If you have worked with healthcare claims, Medicare/Medicaid data, or commercial insurance datasets, make this a focal point of your resume and introductory pitch. It is often the single most valued qualification.
• Structure your case study like a consulting pitch: When presenting a technical case study, do not just talk about algorithms. Start with the business problem, explain your assumptions, present your technical solution, and conclude with the financial or operational impact for the client.
• Be ready for legacy tools: While Milliman uses cutting-edge data science methods, many clients and traditional actuarial workflows still rely on SAS and advanced Excel. Show respect for these tools and demonstrate a willingness to bridge the gap between legacy systems and modern data science pipelines.

• Showcase your R Shiny portfolio: If you have built public-facing or open-source R Shiny applications, share links to them. Being able to demonstrate that you can build functional, visually appealing, and interactive data tools is a major differentiator.
• Ask smart, industry-specific questions: At the end of your interviews, ask questions that show you understand their business model. For example, ask about how they are incorporating machine learning into traditional actuarial pricing models, or how changing healthcare regulations are impacting their data strategy.

Positioning yourself for a Data Scientist role at Milliman requires demonstrating a unique blend of technical mastery and industry-specific consulting acumen. By focusing your preparation on R programming, Shiny application development, healthcare claims structures, and structured case studies, you can show the hiring team that you are ready to deliver immediate value to their clients.

The most successful candidates are those who do not just present themselves as programmers, but as analytical problem-solvers who understand the financial and operational realities of the healthcare and insurance sectors. Take the time to practice your presentation skills, refine your domain knowledge, and approach the interview with a consultative mindset.

The compensation data above reflects the competitive salary structures offered at Milliman. When evaluating an offer, remember that total compensation packages often include performance-based bonuses linked to practice group profitability and individual consulting contributions. To dive deeper into interview questions, company culture reviews, and detailed salary negotiations for this role, explore additional interview insights and resources on Dataford. Focused preparation will materially improve your performance—good luck!