To succeed in the Milliman interview process, you must prepare for an environment that values technical accuracy just as much as clear communication.

Technical Competency – You must demonstrate a strong command of relational databases and data manipulation tools. Expect to prove your SQL skills, advanced Excel capabilities, and familiarity with statistical tools like Python, R, or SAS. Interviewers look for clean, logical code and a systematic approach to data cleaning.

Structured Problem Solving – When presented with a dataset or a case study, your ability to organize your thoughts is critical. You should be able to break down a complex, ambiguous problem into testable hypotheses, perform the necessary analysis, and synthesize your findings into a structured report.

Consulting Mindset & Communication – Because Data Analysts at Milliman frequently support consulting teams, you must be able to explain the "so what" behind the data. Interviewers evaluate how clearly you explain technical concepts and whether you can present data in a way that helps partners make strategic decisions.

Adaptability & Independence – Due to Milliman's decentralized practice structure, you will often work with minimal supervision. You need to show that you are self-directed, comfortable with ambiguity, and capable of managing your own deadlines across multiple projects.

The interview process for a Data Analyst at Milliman is unique because it is highly customized by each office and practice group. While some offices run a streamlined process, others utilize highly rigorous, multi-stage evaluations to match their specific consulting needs. You should prepare for a process that can range from highly technical take-home assessments to extensive panel interviews.

Typically, the process begins with an HR screening call to discuss your background, salary expectations, and interest in the specific practice group. Following this, many teams require a technical assessment. This may take the form of a one-way video interview, an online coding test, or a comprehensive take-home case study where you are given a dataset and asked to spend several hours analyzing it and preparing a professional report.

If you pass the technical screening, you will move on to the final rounds. This stage can be highly intensive. Some practice groups will invite you for a full day of consecutive one-on-one interviews with members of different teams. Other groups may conduct a structured panel day where you meet with up to eight different stakeholders—including Project Managers, Consultants, and Partners—to evaluate your technical depth, behavioral alignment, and consulting potential.

The timeline above outlines the typical progression from the initial application to the final offer stage. Candidates should use this visual guide to pace their preparation, ensuring they allocate ample time for both the take-home case study and the intensive final-round panel interviews. Because the process is managed locally by individual practice offices, response times between stages can vary significantly.

Technical Data Skills

Your technical evaluation is designed to ensure you can handle large, complex datasets with minimal oversight. At Milliman, data integrity is paramount; a single analytical error can lead to millions of dollars in mispriced risk for a client.

Be ready to go over:

• SQL Proficiency – Advanced querying, window functions, complex joins, and database optimization techniques.
• Data Validation – Methods for identifying outliers, handling missing values, and ensuring data quality before running models.
• Reporting & Visualization – Translating raw query outputs into clean Excel models, Power BI dashboards, or client-ready reports.
• Advanced concepts (less common) – Predictive modeling using R or Python, automated ETL pipeline development, and database schema design.

Example questions or scenarios:

• "Review this SQL query containing a subquery and explain how you would rewrite it using a Common Table Expression (CTE) to improve readability."
• "You receive a dataset where 15% of the patient age values are missing. How do you decide whether to impute these values, drop the records, or flag them?"
• "Walk me through how you would set up an automated data-checking script to alert the team when a client sends a corrupted data file."

Case Study & Analytical Reporting

The case study is a cornerstone of the Milliman evaluation process. It simulates a real-world consulting request where you must ingest a raw dataset, perform an analysis, and present your findings to senior leadership.

Be ready to go over:

• Structured Analysis – Defining the business problem, establishing a methodology, and executing the analysis systematically.
• Executive Summaries – Writing clear, concise summaries that highlight key findings, limitations, and recommendations.
• Data Storytelling – Choosing the right charts and tables to make complex data easily digestible for consultants and partners.

Example questions or scenarios:

• "Spend 2-3 hours analyzing this dataset of regional healthcare utilization and prepare a 3-page report highlighting the primary drivers of cost increases."
• "Based on your analysis of this pension plan data, what recommendations would you make to the plan administrators regarding their demographic trends?"
• "Explain the methodology you used in your case study report. Why did you prioritize certain data variables over others?"

Behavioral & Consulting Fit

Working at Milliman requires a balance of technical execution and client-focused professionalism. Interviewers want to see how you collaborate, manage pressure, and interact with senior leadership.

Be ready to go over:

• Managing Ambiguity – How you proceed when client requirements are unclear or data is incomplete.
• Stakeholder Management – Communicating technical limitations or analytical delays to project managers and partners.
• Prioritization – Handling competing demands from different consulting teams or projects.

Example questions or scenarios:

• "Tell me about a time when a project manager gave you a dataset with no instructions on what to look for. How did you start your analysis?"
• "How would you handle a situation where a partner asks you to run an analysis that you know cannot be completed accurately with the available data?"
• "Describe a time when you had to manage multiple tight deadlines simultaneously. How did you organize your tasks to ensure accuracy?"

As a Data Analyst at Milliman, your daily responsibilities will center on ensuring data accuracy, executing complex analyses, and supporting the consulting pipeline. You will act as the technical backbone of your practice group, transforming raw client data into structured assets that consultants can use to advise clients.

Your primary responsibilities will include:

• Data Ingestion and Cleansing – You will receive large, disparate datasets from clients (such as insurance claims, financial records, or demographic data). You must validate, clean, and format this data to ensure it aligns with Milliman's analytical standards.
• Querying and Modeling – You will write complex SQL queries and build analytical models to extract key trends, identify cost drivers, and calculate risk metrics.
• Reporting and Presentation – You will design and maintain reports, dashboards, and client-ready slide decks. You must translate quantitative findings into clear business insights that consultants can present to clients.
• Cross-functional Collaboration – You will collaborate daily with actuaries, consultants, and IT staff. You will help bridge the gap between technical data infrastructure and strategic business applications.
• Process Automation – You will identify opportunities to streamline repetitive data tasks, writing scripts or building macros to automate data validation and reporting workflows.

Because Milliman operates across highly specialized domains like healthcare, insurance, and employee benefits, successful candidates typically possess a strong quantitative background combined with excellent communication skills.

• Must-have skills – Strong proficiency in SQL (writing complex queries, joins, and aggregations); advanced Excel skills (VLOOKUPs, pivot tables, complex formulas, and data modeling); experience handling large, unstructured datasets; and a proven track record of structured problem-solving.
• Nice-to-have skills – Experience with statistical programming languages (Python, R, or SAS); familiarity with business intelligence tools (Power BI, Tableau); and prior exposure to healthcare claims data, insurance underwriting, or actuarial concepts.
• Experience level – Typically requires a bachelor's degree in a quantitative field (such as Statistics, Mathematics, Economics, Finance, or Computer Science) and 1 to 4 years of experience in a data-focused analytical role, preferably within a consulting or professional services environment.
• Soft skills – Strong written and verbal communication; the ability to work independently in a decentralized environment; exceptional attention to detail; and a proactive approach to learning complex business domains.