RELX Data Scientist interview questions & guide 2026

The following questions are representative of the patterns observed in RELX interview loops. While specific technical challenges may shift based on the team—such as commodity markets versus legal analytics—these questions illustrate the core competencies you must demonstrate.

Product-Sense

These questions assess your ability to align data science work with user needs and business objectives.

• How would you design a metric to measure the success of a new search feature?
• A key engagement metric has dropped suddenly; how would you investigate the root cause?

Preparation for RELX should focus on bridging the gap between theoretical knowledge and practical application. You are not just being tested on your ability to code, but on your ability to apply that code to solve real-world professional information problems.

Technical Competency – You must demonstrate mastery over foundational data tools, specifically SQL and statistical packages. Interviewers look for clean, efficient code and a deep understanding of why specific methods are chosen over others.

Analytical Rigor – This involves your ability to approach ambiguous problems systematically. When presented with a case study, focus on defining your assumptions early and validating your logic before diving into the solution.

Communication & Influence – As a Data Scientist, your ability to communicate findings is as important as the model itself. Practice translating technical experimentation pitfalls and statistical significance into clear, actionable advice for business partners.

Professionalism – Given the feedback regarding the interview experience, demonstrate high levels of organization and patience. Maintain a structured approach to your answers, even if the interview process itself feels disjointed or delayed.

The interview loop at RELX typically involves a series of phone and virtual assessments. You should anticipate a progression that begins with a recruiter or initial technical screen, followed by deeper-dive rounds that focus on both your technical portfolio and your ability to navigate team-based projects.

The process is designed to test the full spectrum of a Data Scientist’s capabilities, from basic SQL proficiency to high-level product design. Candidates should be prepared for a pace that can vary; it is common to have multiple rounds that test different skill sets, often involving different interviewers to gauge how you communicate across various functions.

This visual timeline illustrates the typical progression from initial screening to deeper technical assessments. Candidates should use this to pace their study, ensuring they do not front-load all their preparation for the early rounds while neglecting the behavioral or case-study components that appear later.

Data Manipulation & SQL

Your ability to perform complex data extraction is a non-negotiable requirement. You will be evaluated on your efficiency and your ability to write clean, readable code.

• SQL window functions – Essential for time-series analysis and ranking.
• Data cleaning – Handling outliers and noise in professional datasets.
• Performance optimization – Understanding how to write queries that scale.

Example scenarios: "Calculate the week-over-week growth of user logins using a window function" or "Identify the top 5 most active users in each region."

Experimentation & Product Metrics

This is the core of the product-biased Data Scientist role. You must be able to design experiments that are statistically sound and business-relevant.

• Metric drop diagnosis – How to isolate variables during a performance dip.
• Experimentation pitfalls – Identifying selection bias, novelty effects, or Simpson’s paradox.
• Product metric design – Creating North Star metrics that balance growth and quality.

Example scenarios: "How would you validate that a new recommendation algorithm is actually improving user satisfaction?" or "What would you do if your A/B test shows a significant result but the business impact is negligible?"

As a Data Scientist at RELX, your day-to-day work centers on transforming large-scale information into decision-making tools. You will spend a significant portion of your time querying databases to extract insights, cleaning and preparing data for predictive models, and designing A/B tests to validate new feature releases.

Collaboration is a core pillar of the role. You will work alongside product managers to define what success looks like for new initiatives and partner with data engineers to ensure that the data pipelines you rely on are robust and scalable. Whether you are working on specialized commodity market models or improving search relevancy, you are responsible for ensuring that your analytical output is not only accurate but also clearly understood and actionable by your stakeholders.

A strong candidate for RELX combines deep technical skills with a pragmatic, product-focused mindset.

• Technical skills – Strong proficiency in SQL is mandatory. You should also have experience with statistical modeling, Python or R for data analysis, and a solid grasp of A/B testing frameworks.
• Experience level – Experience in roles that required heavy interaction with product or business teams is highly valued. Senior roles, such as Senior Data Scientist II, require a proven track record of leading projects from concept to deployment.
• Soft skills – Strong communication is essential. You must be able to influence stakeholders and clearly articulate the "why" behind your technical decisions.
• Must-have – Mastery of SQL window functions, knowledge of statistical significance testing, and experience in diagnosing experimentation pitfalls.

Q: How long should I prepare for the interview? A: Given the mix of technical and case-study questions, 3–4 weeks of focused preparation is recommended. Ensure you spend time practicing SQL queries and reviewing core statistical concepts.

Q: What is the most common reason candidates fail? A: Candidates often focus too heavily on the "how" (the code) and neglect the "why" (the business impact). Always tie your technical solution back to the product goal or the specific business problem mentioned in the prompt.

Q: Is the interview process difficult? A: Candidates report a moderate-to-difficult level of rigor. The challenge lies in the breadth of topics, ranging from DSA basics to complex product-sense case studies.

Q: How is the work-life balance at RELX? A: Employees generally report very high satisfaction with work-life balance, making it a stable environment for long-term career growth.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to ensure your answers are concise and impactful.
• Be ready for ambiguity: When asked a product question, ask clarifying questions before proposing a solution. This shows you think before you act.
• Focus on the fundamentals: Do not get lost in advanced machine learning theory; the interviewers are more interested in your ability to apply basic statistics and data manipulation correctly.
• Prepare for the administrative side: Be patient with the recruitment process. Maintain your professionalism, even if the process feels slower than expected.

The Data Scientist role at RELX offers a unique opportunity to influence high-impact professional tools used globally. By mastering the core technical requirements—specifically SQL window functions, A/B testing, and metric diagnosis—you position yourself as a candidate who can deliver immediate value to the organization.

Preparation is the primary differentiator between candidates. By focusing on the structural patterns outlined in this guide, you can approach your interviews with confidence and clarity. You can explore additional interview insights, practice questions, and preparation resources on Dataford to further sharpen your skills and improve your performance.

The compensation data provided above reflects the range for various seniority levels within RELX. Candidates should interpret this as a baseline that can be influenced by location, specific technical expertise, and years of relevant experience.