Gallagher Data Scientist interview questions & guide 2026

The following questions are representative of the patterns observed in Gallagher interview loops. While specific technical focuses may shift based on the hiring team, you should prepare for a blend of rigorous technical assessment and situational product-sense questions.

SQL and Data Manipulation

These questions test your ability to extract and transform data to solve real-world problems, with a specific focus on window functions for complex reporting.

• How would you use a window function to calculate a rolling 30-day average for claims data?
• Explain the difference between RANK() and DENSE_RANK() in a SQL query.

Success at Gallagher depends on your ability to combine technical rigor with a pragmatic, business-first mindset. Preparation should be structured around demonstrating both depth of knowledge and the ability to drive projects forward in a collaborative environment.

Technical Proficiency – You must be comfortable with the entire data lifecycle, from SQL data extraction to model evaluation. Interviewers look for clean, efficient code and a deep understanding of the statistical assumptions behind your models.

Problem-Solving Approach – When faced with an ambiguous case study, structure your thoughts clearly. Start by defining the goal, identifying the necessary data, proposing a methodology, and acknowledging potential limitations or risks.

Communication and Influence – At Gallagher, your ability to convey the "why" behind your data is as important as the "how." Be prepared to explain how your work impacts the company's bottom line and how you navigate feedback from cross-functional teams.

Leadership and Adaptability – Demonstrating maturity in handling setbacks or conflicting requirements is essential. Focus on your ability to remain objective and solution-oriented even when project scopes or requirements shift.

The interview loop at Gallagher is typically designed to assess your technical fundamentals and your fit for a collaborative, fast-paced environment. Candidates usually navigate a series of virtual interactions, which may include a recruiter screen, a technical assessment or coding round, and a final managerial interview. The process is intended to be straightforward, focusing heavily on your practical application of data science concepts to domain-specific problems like risk and customer management.

This timeline illustrates a standard progression from initial engagement to final decision-making. Use this as a framework to manage your preparation pace, ensuring you have enough time to brush up on both your technical implementation skills and your ability to articulate your past experiences.

SQL and Data Manipulation

This is the baseline for the role. You are expected to be fluent in writing complex queries to manipulate large datasets efficiently.

• Window Functions – Crucial for time-series analysis and partitioning.
• Query Optimization – Understanding execution plans and indexing.
• Handling Data Quality – Proactively addressing nulls, duplicates, and outliers.

A/B Testing and Experimentation

You will be evaluated on your ability to design valid experiments and correctly interpret results to inform product decisions.

• Experimental Design – Defining control vs. treatment and randomization.
• Statistical Significance – Ensuring findings are robust.
• Diagnostic Frameworks – Identifying root causes when metrics trend unexpectedly.

Product Sense and Metrics

This area tests your ability to map business challenges to data solutions.

• Metric Design – Creating KPIs that align with user value.
• Prioritization – Assessing the trade-offs between model complexity and speed to market.
• Scenario Analysis – Walking through the impact of a model change on a business process.

As a Data Scientist at Gallagher, your day-to-day work involves more than just model building. You will be responsible for defining the analytical approach to business problems, which requires deep collaboration with product and engineering teams. You will frequently work on identifying patterns in customer data to improve risk assessment models, which are central to the company's operations.

Your deliverables will often include the end-to-end development of analytical products, from initial data exploration and pipeline design to model deployment and monitoring. You will be expected to maintain high standards for technical documentation and to present your results to stakeholders, ensuring that the insights you generate are understood and effectively integrated into company-wide initiatives.

A strong candidate for this role possesses a solid academic foundation in a quantitative field combined with practical experience in applying machine learning to real-world datasets.

• Must-have skills
  • Proficiency in SQL (including advanced window functions).
  • Strong foundation in A/B testing and statistical inference.
  • Proven ability to design and monitor product metrics.
  • Experience in Python or R for data manipulation and modeling.
• Nice-to-have skills
  • Familiarity with RAG architecture and Prompt Engineering (as these have appeared in recent technical loops).
  • Exposure to GenAI model implementation.
  • Experience within the insurance or risk management sectors.

Q: How long does the interview process typically take? The process varies, but it is generally designed to move efficiently once you are in the pipeline. Expect a total duration of 2 to 4 weeks from the initial screening to a final decision.

Q: Are there specific technical tools I should master? Focus on mastering SQL and your language of choice (Python or R). Additionally, be prepared to discuss the mathematical theory behind the models you use, as the team values depth of understanding over simple library usage.

Q: What differentiates a good candidate from a great one? Great candidates at Gallagher don't just solve the technical problem—they explain the business impact of their solution. They demonstrate a high degree of ownership and the ability to navigate ambiguity when requirements change.

Q: How should I prepare for the behavioral rounds? Use the STAR method (Situation, Task, Action, Result) to structure your answers. Focus on examples that highlight your ability to collaborate, influence others, and handle project-related challenges.

• Verify the Details: Always ask for the specific expectations of the role during your first interaction with the recruiter to ensure alignment.
• Be Ready for Ambiguity: If an interviewer gives you a vague problem, ask clarifying questions before jumping into a solution. This is a key part of the evaluation.
• Focus on the "Why": Don't just explain the model you built; explain why it was the right choice for that specific business problem.
• Review Fundamentals: Do not underestimate the importance of basic statistics and probability—these are often used to test your foundational knowledge before moving to complex topics.

The Data Scientist role at Gallagher is an excellent opportunity to apply sophisticated analytical techniques to high-impact business problems. By mastering the fundamentals of SQL, A/B testing, and metric design, and by preparing to clearly communicate your strategic thinking, you can significantly increase your chances of success.

Candidates can explore additional interview insights, practice questions, and preparation resources on Dataford. Remember that thorough preparation is the most effective way to navigate the interview process with confidence.

The compensation data provided above reflects typical market ranges for this role. Candidates should interpret these figures as a guideline, taking into account factors such as regional cost of living, years of experience, and the specific seniority level of the position.