Gallagher Data Scientist interview questions & guide 2026

The following questions are representative of the patterns observed in recent interview cycles. While interviewers may adapt their focus based on current team needs, you should prepare for a rigorous evaluation that tests both your theoretical foundations and your ability to apply those concepts to real-world business scenarios.

SQL and Data Manipulation

These questions test your ability to extract and transform data efficiently, which is a foundational requirement for the role.

• How would you use SQL window functions to calculate a rolling average of customer churn rates over the last six months?
• Can you explain the difference between a LEFT JOIN and an INNER JOIN in the context of merging large, sparse datasets?

Preparation for Gallagher requires a disciplined approach that balances technical mastery with business context. You should treat every interview as an opportunity to demonstrate not just your coding or math skills, but your ability to drive the business forward through data.

Technical Competency – You must be fluent in the tools of the trade, specifically SQL and statistical libraries. Interviewers will look for your ability to write clean, efficient code and your depth of knowledge regarding machine learning and GenAI applications.

Problem-Solving Structure – When faced with an ambiguous case study, do not jump straight to a solution. Demonstrate a structured, hypothesis-driven approach that defines the problem, lists assumptions, and outlines your methodology before diving into the details.

Communication and Influence – Your ability to articulate the "why" behind your analysis is as important as the "how." Practice translating technical trade-offs into business outcomes, as this is a core expectation for Data Scientists at this level.

Alignment with Business Reality – Understand that Gallagher is a risk-management firm. Frame your answers to show that you understand the stakes of your analysis, whether it involves customer acquisition, risk modeling, or operational efficiency.

The interview process at Gallagher is typically structured to assess your technical depth, your ability to handle ambiguous problems, and your cultural alignment with the team. Candidates generally progress through a series of stages that include an initial recruiter screen, one or more technical assessments or interviews, and a final managerial or behavioral round.

Expect a process that values efficiency but demands high levels of clarity. You may be asked to solve technical problems in real-time or discuss your past work in detail. Because the process can vary by team, ensure you clarify the format of each round with your recruiter well in advance to avoid surprises.

This visual timeline illustrates the typical progression from initial screening to the final decision. Use this to pace your preparation, ensuring you have allocated enough time to brush up on both your technical fundamentals and your behavioral stories before the later stages.

Experimentation and Statistics

This is a cornerstone of the role. You will be evaluated on your ability to design robust tests that yield reliable, actionable results.

Be ready to go over:

• Statistical significance and power analysis.
• Identifying and correcting for experimentation pitfalls, such as selection bias or novelty effects.

As a Data Scientist at Gallagher, your day-to-day work involves bridging the gap between raw data and strategic decision-making. You will work closely with product managers, engineers, and risk officers to identify opportunities where data can solve high-impact problems.

• Data Exploration and Modeling: You will spend significant time cleaning, analyzing, and modeling large datasets to uncover trends in customer behavior or risk profiles.
• Experimentation: You will design and oversee A/B tests to validate new product features or algorithmic changes, ensuring that changes are backed by statistically sound evidence.
• Cross-functional Collaboration: You will act as a consultant to other departments, helping them understand what data is available, what it means, and how it can be used to achieve their goals.
• Communication: You will be expected to present your findings in a clear, concise manner, often creating visualizations or dashboards that make complex insights accessible to leadership.

A strong candidate for this role possesses a blend of deep technical expertise and a pragmatic, business-first mindset.

• Must-have skills:
  • Proficiency in SQL, including advanced window functions.
  • Strong foundation in statistics and probability, specifically regarding A/B testing.
  • Experience with machine learning frameworks and model deployment.
  • Ability to translate business problems into technical requirements.
• Nice-to-have skills:
  • Exposure to GenAI and RAG architecture (Prompt Engineering).
  • Domain knowledge in insurance or risk management.
  • Experience with cloud-based data platforms (e.g., AWS, GCP, or Azure).

Q: How long is the typical interview process? The process varies, but usually spans a few weeks. It typically includes an initial screen followed by 2–3 rounds of technical and behavioral interviews.

Q: What is the most important factor in being successful at Gallagher? Beyond technical proficiency, the ability to communicate clearly and manage expectations is paramount. Successful candidates show that they can work through ambiguity without losing sight of the business objective.

Q: Should I expect coding tests? Yes, technical assessment is a core part of the process. Be prepared to write SQL and potentially solve machine learning or statistical problems in a live or take-home format.

Q: What is the company culture like? Gallagher values professional, data-driven decision-making. You will be expected to be accountable for your work and to advocate for your findings with evidence and clarity.

• Verify everything: Given the potential for miscommunication regarding role details, always ask for written confirmation of the job's scope and status.
• Be ready for behavioral questions: Do not neglect this area; use the STAR method (Situation, Task, Action, Result) to structure your answers.
• Focus on the 'Why': When solving a technical problem, explain your thought process out loud. Interviewers care as much about how you arrive at a solution as the solution itself.

The Data Scientist role at Gallagher is a high-impact position that offers the opportunity to influence major business decisions through rigorous analysis. By focusing on your core technical skills, mastering the art of experimental design, and honing your ability to communicate complex insights, you will position yourself as a top-tier candidate.

Remember that preparation is the most effective tool you have. You can explore additional interview insights, practice questions, and preparation resources on Dataford to sharpen your skills further. Stay focused, be precise in your communication, and approach each round with confidence.

The compensation data provided reflects market trends for similar roles. Use this as a benchmark for your own research, keeping in mind that total compensation packages may vary based on your experience level, specific team requirements, and location.