Thorough preparation is the key to navigating the interview process confidently. Your interviewers will look for a balance of modern machine learning expertise, domain awareness, and strong communication skills.

Focus your preparation on the following key evaluation criteria:

Technical & Domain Expertise You must demonstrate a strong command of traditional machine learning, statistical modeling, and modern AI architectures. Interviewers will specifically evaluate your knowledge of Generative AI, RAG frameworks, and prompt engineering, alongside your ability to handle complex customer data and risk management scenarios.

Problem-Solving & System Design This measures your ability to translate abstract business problems—like identifying risk exposure or automating document processing—into scalable data science solutions. You should be able to structure your approach logically, select the right algorithms, and design robust data pipelines.

Business Acumen & Contextual Application Arthur J. Gallagher & highly values candidates who understand the "why" behind the models. You will be evaluated on your ability to tie technical metrics back to business outcomes, such as improving customer retention, optimizing pricing, or reducing risk exposure.

Culture Fit & Collaboration Your interviewers want to see how you navigate ambiguity, work within cross-functional teams, and communicate. You can demonstrate strength here by sharing examples of how you have successfully explained technical constraints to business leaders and adapted to changing project requirements.

The interview process for a Data Scientist at Arthur J. Gallagher & is designed to be straightforward, efficient, and highly relevant to the day-to-day work you will perform. The firm typically moves quickly, with a focus on practical technical skills and cultural alignment. You can expect a process that spans two to three distinct rounds, usually completed within a few weeks.

Your journey will generally begin with a virtual 30-minute screening round, which focuses on your background, high-level technical experience, and basic personal questions. This is followed by a comprehensive technical round, which lasts about an hour. Depending on the team, this round may involve a live assessment or an in-depth discussion of AI/ML concepts, specifically leaning into customer data applications, risk management, and modern GenAI architectures.

The final stage is typically a Managerial or HR round. This conversation shifts away from technical minutiae and focuses heavily on behavioral questions, culture fit, and your ability to thrive in a corporate environment that values both innovation and risk management.

This visual timeline outlines the typical progression from the initial recruiter screen through the technical assessments and final managerial rounds. You should use this to pace your preparation, focusing heavily on core ML and GenAI concepts for the middle stages, and shifting your focus toward behavioral and business-impact narratives for the final round. Keep in mind that specific technical assessments may vary slightly depending on the regional office or specific team focus.

To succeed, you need to understand exactly what your interviewers are looking for in each technical and behavioral domain. The interviews at Arthur J. Gallagher & blend traditional data science with emerging AI trends.

Generative AI and LLM Architecture

With the rapid adoption of AI in the insurance sector, Arthur J. Gallagher & places a significant emphasis on Generative AI. This area evaluates your practical experience in building and deploying modern language models to solve real business problems, such as document analysis or automated advisory systems. Strong performance means moving beyond basic API calls to understanding the underlying architecture.

Be ready to go over:

• Retrieval-Augmented Generation (RAG) – Understanding how to build robust RAG pipelines, chunking strategies, vector databases, and managing context windows.
• Prompt Engineering – Techniques for optimizing prompts to ensure accurate, hallucination-free outputs, especially critical in risk and compliance contexts.
• LLM Fine-Tuning vs. Few-Shot Learning – Knowing when to fine-tune a model versus when to rely on context injection, and the trade-offs in cost and performance.
• Advanced concepts (less common) – Evaluation metrics for LLMs (e.g., RAGAS), agentic workflows, and deploying open-source models securely.

Example questions or scenarios:

• "Walk me through the architecture of a RAG system you built. How did you handle document retrieval and ranking?"
• "How would you design a prompt engineering strategy to extract specific risk clauses from a 100-page insurance policy?"
• "What methods do you use to evaluate the accuracy and reliability of a Generative AI model's output?"

Core Machine Learning & Risk Modeling

Despite the rise of GenAI, traditional machine learning remains the backbone of predictive analytics at the firm. This area tests your ability to handle structured customer data and build models that accurately forecast risk, churn, or pricing elasticity. A strong candidate will demonstrate a deep understanding of model selection, feature engineering, and validation.

Be ready to go over:

• Supervised Learning – Deep knowledge of classification and regression algorithms (e.g., XGBoost, Random Forests, Logistic Regression) and their mathematical foundations.
• Customer Data Analytics – Techniques for handling imbalanced datasets, feature scaling, and extracting signals from noisy customer behavior data.
• Model Explainability – Using tools like SHAP or LIME to explain model decisions to non-technical stakeholders, a critical requirement in insurance.
• Advanced concepts (less common) – Survival analysis, time-series forecasting for market trends, and causal inference.

Example questions or scenarios:

• "How would you approach building a model to predict customer churn using historical policy data?"
• "Explain how you handle highly imbalanced datasets, which are common in fraud or rare-event risk modeling."
• "If your model predicts a high risk for a major client, how do you explain the driving factors to a brokerage manager?"

Behavioral and Culture Fit

Arthur J. Gallagher & values professionals who are collaborative, adaptable, and ethically grounded. This evaluation area focuses on your past experiences, your problem-solving mindset, and your ability to work within a regulated but innovative environment. Strong candidates provide structured, concise answers that highlight their impact and leadership.

Be ready to go over:

• Stakeholder Management – How you align technical deliverables with business expectations and handle pushback.
• Adaptability – Your ability to pivot when project requirements change or when data is unavailable.
• Cross-functional Collaboration – Examples of working alongside data engineers, software developers, and business analysts.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex machine learning concept to a non-technical stakeholder."
• "Describe a situation where a project failed or a model did not perform as expected. How did you handle it?"
• "How do you prioritize your tasks when dealing with multiple urgent requests from different business units?"

As a Data Scientist at Arthur J. Gallagher &, your day-to-day work will revolve around transforming complex, disparate datasets into actionable intelligence. You will spend a significant portion of your time exploring customer data, claims history, and market trends to identify patterns that inform risk management strategies. This involves writing robust Python code, querying large databases using SQL, and designing efficient data processing pipelines.

A major focus of your role will be developing and deploying advanced AI solutions. You will design Retrieval-Augmented Generation (RAG) architectures to help brokers quickly extract insights from dense insurance policies and regulatory documents. This requires constant experimentation with prompt engineering, vector search optimization, and LLM integration, ensuring that the AI tools you build are both highly accurate and compliant with industry regulations.

Collaboration is a cornerstone of this position. You will work hand-in-hand with data engineers to productionize your models, ensuring they scale effectively. Additionally, you will partner with product managers and business leaders to understand their pain points, translate those into technical requirements, and present your findings in a clear, visually compelling manner that drives executive decision-making.

To be competitive for the Data Scientist role at Arthur J. Gallagher &, you must bring a solid mix of technical rigor and business intuition.

• Must-have skills – Proficiency in Python and SQL. Deep understanding of machine learning frameworks (Scikit-learn, XGBoost, PyTorch/TensorFlow). Hands-on experience with Generative AI concepts, specifically RAG architectures, LLMs, and prompt engineering. Strong statistical foundation and ability to manipulate complex customer datasets.
• Experience level – Typically requires 3+ years of industry experience in data science or machine learning roles. A Master's or Ph.D. in a quantitative field (Computer Science, Statistics, Mathematics) is highly preferred, though equivalent practical experience is valued.
• Soft skills – Exceptional communication skills, with a proven ability to translate technical metrics into business value. Strong stakeholder management and the ability to work autonomously in a fast-paced environment.
• Nice-to-have skills – Prior experience in the insurance, fintech, or risk management sectors. Familiarity with cloud platforms (AWS, Azure) and ML model deployment (MLOps). Experience with NLP libraries (Hugging Face, spaCy) and vector databases (Pinecone, Milvus).