Milliman ML Engineer Interview Guide

Getting ready for a Machine Learning Engineer interview at Milliman? The Milliman ML Engineer interview process typically spans 4–6 question topics and evaluates skills in areas like machine learning system design, statistical modeling, coding and algorithmic problem solving, and communicating technical concepts to diverse stakeholders. Interview preparation is particularly important for this role at Milliman, as candidates are expected to demonstrate how they can build robust, scalable ML solutions for complex business challenges, while translating data-driven insights into actionable recommendations that align with client needs and regulatory requirements.

In preparing for the interview, you should:

• Understand the core skills necessary for Machine Learning Engineer positions at Milliman.
• Gain insights into Milliman’s ML Engineer interview structure and process.
• Practice real Milliman ML Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Milliman ML Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Milliman is a global leader in actuarial consulting and risk management, serving clients in the insurance, employee benefits, healthcare, and financial services industries. The company leverages advanced analytics and technology to help organizations assess risk, optimize operations, and comply with regulatory requirements. With a strong focus on data-driven decision-making, Milliman’s mission is to empower clients to navigate complex financial and health-related challenges. As an ML Engineer, you will contribute to developing innovative machine learning solutions that enhance Milliman’s analytical capabilities and drive impactful outcomes for its diverse client base.

As an ML Engineer at Milliman, you will design, develop, and deploy machine learning models to support data-driven decision-making in the insurance and healthcare sectors. Your responsibilities include collaborating with data scientists, actuaries, and software engineers to build scalable solutions that improve risk assessment, pricing models, and predictive analytics. You will work with large datasets, implement advanced algorithms, and ensure model accuracy and reliability in production environments. This role is crucial for advancing Milliman’s analytical capabilities and helping clients make informed, evidence-based decisions through innovative machine learning applications.

2.1 Stage 1: Application & Resume Review

Milliman’s ML Engineer application process begins with a thorough screening of your resume and cover letter to assess your experience with machine learning frameworks, statistical modeling, data engineering, and deployment of models in production environments. The recruiting team and hiring manager will look for evidence of hands-on work with neural networks, kernel methods, and real-world data projects, as well as your ability to communicate technical concepts clearly.

2.2 Stage 2: Recruiter Screen

The recruiter screen typically consists of a 30-minute phone or video call with a Milliman recruiter. This conversation focuses on your motivation for applying, your understanding of the ML Engineer role, and a high-level review of your technical background. Expect questions about your experience with Python, SQL, and data cleaning, as well as your interest in Milliman’s business areas and culture. Preparation should include a concise summary of your relevant skills and a clear articulation of why you want to join Milliman.

2.3 Stage 3: Technical/Case/Skills Round

This stage is designed to rigorously assess your core machine learning and engineering competencies. You may encounter a mix of coding exercises, case studies, and theoretical questions, often conducted by senior ML engineers or data scientists. You should be prepared to demonstrate your ability to implement algorithms from scratch (such as logistic regression), solve data manipulation problems, and discuss approaches to real-world ML system design (e.g., unsafe content detection, sentiment analysis, feature store integration). Expect to explain complex concepts like neural networks, kernel methods, and handling imbalanced data, as well as to present solutions for business-oriented ML cases such as ride-sharing promotions or e-commerce content generation. Preparation should involve reviewing your technical fundamentals and practicing the communication of your thought process.

2.4 Stage 4: Behavioral Interview

The behavioral interview is typically conducted by the hiring manager and/or team leads. This session explores your teamwork, communication, adaptability, and problem-solving skills in the context of Milliman’s collaborative and client-driven environment. You may be asked about challenges you’ve faced in data projects, ways you present insights to non-technical stakeholders, and how you approach ethical considerations in ML model deployment. Prepare by reflecting on past experiences where you demonstrated initiative, adaptability, and clear communication.

2.5 Stage 5: Final/Onsite Round

The final round usually consists of 2-4 interviews with cross-functional team members, including senior engineers, data scientists, and business stakeholders. Expect a mix of technical deep-dives, system design discussions, and business case analysis. You may be asked to walk through end-to-end ML project workflows, assess tradeoffs in model design, and discuss strategies for integrating ML tools into existing business processes. There may also be a presentation component, where you’ll need to communicate complex findings to both technical and non-technical audiences. Preparation should focus on synthesizing your technical and business acumen, and demonstrating your fit for Milliman’s client-focused approach.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, the recruiter will reach out to discuss the offer package, benefits, and onboarding logistics. This is your opportunity to clarify compensation, role expectations, and team structure. Preparation here should include researching industry benchmarks and preparing thoughtful questions about Milliman’s career development opportunities.

2.7 Average Timeline

The typical Milliman ML Engineer interview process spans 3-5 weeks from initial application to final offer, depending on scheduling and team availability. Fast-track candidates with highly relevant skills and clear communication may progress in as little as 2-3 weeks, while standard timelines allow for a week or more between each stage. Take-home assignments or technical screens are usually given a 3-5 day window for completion, and onsite rounds are scheduled based on the availability of interviewers.

Next, let’s review the specific interview questions you may encounter at each stage of the Milliman ML Engineer process.

3.1 Machine Learning Concepts & Modeling

Expect questions that probe your understanding of model selection, evaluation, and deployment, with particular attention to real-world business impact and technical rigor. Milliman values engineers who can balance theoretical depth with practical decision-making in ML solutions.

3.1.1 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Frame your answer around experimental design, identifying key metrics such as retention, revenue impact, and customer acquisition. Discuss how you would use A/B testing and causal inference to measure the promotion's effectiveness.

3.1.2 Identify requirements for a machine learning model that predicts subway transit
List essential features, data sources, and evaluation metrics for the predictive model. Emphasize system constraints, feature engineering, and how you would handle temporal or spatial data.

3.1.3 Building a model to predict if a driver on Uber will accept a ride request or not
Describe your approach to feature selection, data labeling, and model evaluation. Address class imbalance and discuss how you would validate results in production.

3.1.4 How would you approach the business and technical implications of deploying a multi-modal generative AI tool for e-commerce content generation, and address its potential biases?
Discuss the integration of multi-modal data, bias detection and mitigation strategies, and stakeholder alignment. Highlight how you would monitor outputs and ensure ethical deployment.

3.1.5 Designing an ML system for unsafe content detection
Explain system architecture for content moderation, including data labeling, model selection, and evaluation. Discuss scalability and how you would handle edge cases or adversarial inputs.

3.2 Deep Learning & Neural Networks

Milliman looks for engineers who can clearly communicate complex neural network concepts and justify their use in practical scenarios. Expect to demonstrate both technical fluency and the ability to educate stakeholders.

3.2.1 Explain Neural Nets to Kids
Break down neural networks using simple analogies and real-world examples. Focus on clarity and accessibility for non-technical audiences.

3.2.2 Justify a Neural Network
Describe why a neural network is the appropriate choice for a given problem, comparing alternatives. Highlight the tradeoffs in accuracy, interpretability, and computational cost.

3.2.3 Kernel Methods
Explain the concept of kernel methods and their application in ML. Discuss scenarios where they outperform traditional models and why.

3.2.4 Why would one algorithm generate different success rates with the same dataset?
Discuss factors such as random initialization, hyperparameter selection, and data splits. Emphasize the importance of reproducibility and robust validation.

3.3 Data Engineering & System Design

You’ll be asked to demonstrate your ability to design scalable data systems, efficiently process large datasets, and ensure data quality. Milliman emphasizes maintainability and reliability in engineering solutions.

3.3.1 Modifying a billion rows
Outline strategies for efficiently updating massive datasets, such as batching, parallelization, and indexing. Discuss the importance of minimizing downtime and ensuring data integrity.

3.3.2 Design a feature store for credit risk ML models and integrate it with SageMaker.
Describe key components of a feature store, integration with ML pipelines, and best practices for versioning and monitoring. Address scalability and security considerations.

3.3.3 Write a function to return the names and ids for ids that we haven't scraped yet.
Discuss efficient data retrieval, deduplication, and incremental data processing. Emphasize maintainable code and error handling.

3.3.4 Design a data warehouse for a new online retailer
Explain schema design, ETL processes, and how to enable fast analytics. Focus on scalability, data governance, and support for ML workflows.

3.4 Experimental Design & Statistical Analysis

Milliman expects ML engineers to be fluent in statistical reasoning and experimental design, ensuring insights are actionable and reliable. Prepare to discuss data sampling, hypothesis testing, and metrics selection.

3.4.1 How would you measure the success of an email campaign?
Describe key performance indicators, control groups, and statistical testing. Discuss how you would attribute impact and communicate results.

3.4.2 Addressing imbalanced data in machine learning through carefully prepared techniques.
Explain strategies for handling imbalanced datasets, such as resampling, weighting, and using appropriate metrics. Emphasize validation and avoiding bias.

3.4.3 Write a function to get a sample from a Bernoulli trial.
Show your understanding of probability distributions and random sampling. Highlight how this ties into model evaluation or simulation tasks.

3.4.4 Write a function to get a sample from a standard normal distribution.
Explain the mechanics of generating samples and its applications in ML, such as bootstrapping or synthetic data generation.

3.4.5 Maximum Profit
Discuss how to approach optimization problems using statistical or ML methods. Address constraints and sensitivity analysis.

3.5 Behavioral Questions

3.5.1 Tell Me About a Time You Used Data to Make a Decision
Describe a situation where your analysis led directly to a business or product change. Focus on how you translated findings into actionable recommendations and measured impact.

3.5.2 Describe a Challenging Data Project and How You Handled It
Share a detailed example of a complex project, highlighting obstacles, your problem-solving strategy, and the final outcome.

3.5.3 How Do You Handle Unclear Requirements or Ambiguity?
Explain your process for clarifying goals, collaborating with stakeholders, and iterating on solutions when faced with incomplete information.

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Discuss your communication style, how you fostered collaboration, and the resolution that was reached.

3.5.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Outline your prioritization framework and communication strategies for managing expectations while protecting project integrity.

3.5.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Explain how you balanced transparency with proactive planning, and how you maintained trust.

3.5.7 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly
Describe trade-offs you made, how you communicated risks, and what steps you took to ensure future quality.

3.5.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation
Share your approach to persuasion, leveraging data, and building consensus.

3.5.9 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth
Discuss your process for aligning stakeholders and standardizing metrics.

3.5.10 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Highlight your prioritization framework, negotiation methods, and communication strategies.

4.1 Company-specific tips:

Demonstrate your understanding of Milliman’s core business domains—insurance, healthcare, employee benefits, and financial services. Familiarize yourself with how machine learning and advanced analytics are transforming risk management, pricing, and predictive modeling in these industries. Be ready to discuss recent trends or regulatory changes that impact Milliman’s clients, and how ML solutions can address those challenges.

Highlight your ability to communicate technical concepts to non-technical stakeholders, such as actuaries and business leaders. Milliman values engineers who can bridge the gap between data science and client needs, so practice explaining ML models, results, and limitations in clear, business-oriented language.

Showcase your experience with compliance and ethical considerations in ML deployment. Milliman operates in highly regulated environments, so be prepared to discuss data privacy, fairness, and model transparency, especially when building solutions for insurance or healthcare clients.

Research Milliman’s recent projects, publications, or case studies related to machine learning. Reference these in your interview to demonstrate genuine interest and alignment with the company’s mission to empower clients through data-driven insights.

4.2 Role-specific tips:

4.2.1 Prepare to design end-to-end ML systems for real-world business problems.
Practice walking through the design of machine learning systems from data ingestion to model deployment, especially for use cases like risk assessment, pricing models, or unsafe content detection. Be ready to discuss feature engineering, model selection, and how you would monitor and maintain models in production.

4.2.2 Brush up on statistical modeling and experimental design.
Expect questions that assess your ability to structure experiments, select appropriate metrics, and evaluate model performance. Review concepts like A/B testing, causal inference, and handling imbalanced data, as these are crucial for evaluating promotions, campaigns, and predictive models in Milliman’s business context.

4.2.3 Strengthen your coding and algorithmic problem-solving skills.
Milliman interviews often include coding exercises focused on data manipulation, algorithm implementation, and efficiency. Practice writing clean, maintainable code in Python or SQL, and be ready to tackle problems like updating massive datasets, designing feature stores, or building sampling functions for statistical analysis.

4.2.4 Be able to clearly explain deep learning and kernel methods.
You’ll need to articulate when and why to use neural networks versus other approaches, and justify your choices in terms of accuracy, interpretability, and computational cost. Prepare analogies and real-world examples to explain complex concepts to both technical and non-technical audiences.

4.2.5 Show your approach to system design and scalability.
Milliman values robust, scalable engineering solutions. Be ready to discuss how you would design data pipelines, feature stores, or data warehouses for large-scale ML applications. Highlight strategies for ensuring data quality, reliability, and maintainability.

4.2.6 Practice communicating trade-offs and business impact.
Prepare to discuss how you balance short-term business needs with long-term data integrity and model reliability. Be ready with examples of how you’ve prioritized projects, managed stakeholder expectations, and translated data insights into actionable recommendations.

4.2.7 Reflect on experiences collaborating across disciplines.
Milliman ML Engineers work closely with actuaries, data scientists, and business teams. Think of examples where you successfully navigated ambiguous requirements, resolved conflicting priorities, or influenced stakeholders without formal authority. Focus on your ability to build consensus and drive adoption of data-driven solutions.

4.2.8 Be ready to address ethical and regulatory considerations in ML projects.
Prepare to discuss how you identify and mitigate bias, ensure fairness, and maintain compliance with privacy regulations in your ML workflows. Milliman’s clients rely on trustworthy, transparent models—show that you prioritize these values in your engineering approach.

5.1 How hard is the Milliman ML Engineer interview?
The Milliman ML Engineer interview is challenging, especially for those new to regulated industries like insurance and healthcare. Expect rigorous evaluation of your machine learning fundamentals, system design skills, and your ability to communicate technical concepts to non-technical stakeholders. Milliman values depth in statistical modeling, practical experience deploying ML solutions, and clear business impact. Candidates who can demonstrate both technical excellence and business alignment will stand out.

5.2 How many interview rounds does Milliman have for ML Engineer?
Milliman’s ML Engineer interview process typically spans 4–6 rounds. These include a recruiter screen, technical/case interviews, a behavioral interview, and a final onsite round with cross-functional team members. Each stage is designed to assess different facets of your experience, from coding and statistical analysis to communication and collaboration.

5.3 Does Milliman ask for take-home assignments for ML Engineer?
Yes, Milliman may include a take-home assignment or technical screen as part of the process. These assignments often focus on real-world machine learning problems, such as building a predictive model or designing a scalable data pipeline. You’ll be expected to demonstrate your coding skills, analytical thinking, and ability to deliver robust solutions under realistic constraints.

5.4 What skills are required for the Milliman ML Engineer?
Core skills for Milliman ML Engineers include expertise in machine learning algorithms, statistical modeling, coding (Python, SQL), data engineering, and system design. Experience with deep learning, kernel methods, and handling large, messy datasets is highly valued. Strong communication skills are essential, as you’ll need to translate complex ML concepts for business and actuarial audiences. Familiarity with compliance, ethical ML deployment, and working in regulated environments is a plus.

5.5 How long does the Milliman ML Engineer hiring process take?
The typical timeline for the Milliman ML Engineer hiring process is 3–5 weeks from initial application to final offer. This can vary depending on candidate and team availability, with fast-track candidates progressing more quickly. Take-home assignments usually have a 3–5 day completion window, and onsite rounds are scheduled based on interviewer availability.

5.6 What types of questions are asked in the Milliman ML Engineer interview?
Expect a mix of technical, business case, and behavioral questions. Technical questions cover machine learning concepts, coding exercises, system design, and statistical analysis. Business cases often focus on real-world ML applications in insurance or healthcare, such as risk modeling or content moderation. Behavioral questions assess teamwork, communication, and your approach to ethical and regulatory challenges in ML deployment.

5.7 Does Milliman give feedback after the ML Engineer interview?
Milliman typically provides feedback through recruiters, especially after technical or onsite rounds. While feedback may be high-level, it often highlights strengths and areas for improvement. Detailed technical feedback is less common, but you can always request additional insights to guide your future preparation.

5.8 What is the acceptance rate for Milliman ML Engineer applicants?
Milliman’s ML Engineer role is competitive, with an estimated acceptance rate of 3–7% for qualified applicants. The company seeks candidates with strong technical backgrounds, industry alignment, and excellent communication skills. Thorough preparation and a clear understanding of Milliman’s business domains will help you stand out.

5.9 Does Milliman hire remote ML Engineer positions?
Yes, Milliman does offer remote ML Engineer positions, depending on team needs and project requirements. Some roles may require occasional office visits for collaboration, but the company supports flexible work arrangements to attract top talent across geographies. Always clarify remote work expectations during your interview process.

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Ready to Ace Your Interview?

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