Good Business Lab Research Analyst interview questions & guide 2026

The interview process at Good Business Lab is designed to evaluate your technical proficiency in microeconometrics and your practical grasp of research design. The following categories represent the core areas you should prepare for, based on patterns from recent candidate experiences.

Research Methodology & RCTs

This category tests your fundamental understanding of causal inference and experimental design. Expect to be challenged on the theory and execution of field experiments.

• Define Randomized Controlled Trials (RCTs) and explain their importance.
• How do you conduct and interpret balance tests?

Preparation for this role requires a balance of deep technical mastery and clear communication skills. You should be prepared to discuss not just "how" to run a test, but "why" you chose a specific methodology.

Technical Competency – You must demonstrate fluency in Stata or R. Interviewers are looking for your ability to perform end-to-end data operations, from initial cleaning to complex statistical interpretation, under time constraints.

Research Design Intuition – Beyond the code, you need to show you understand the nuances of field research. This includes understanding the trade-offs in survey design, sampling strategies, and the structural requirements for successful RCTs.

Problem-Solving & Analytical Rigor – You will be evaluated on your ability to look at a dataset or a model and provide an objective, data-backed conclusion. Be prepared to defend your analytical choices and admit when a model shows an intervention did not have the intended effect.

The hiring process at Good Business Lab is structured to be rigorous yet transparent. It typically follows a linear path: an initial CV screening, a data-focused assessment task, followed by one or more technical interviews, and finally an HR-led discussion. The pace is generally steady, though it can take several weeks from the initial application to a final decision.

The philosophy here is heavily weighted toward practical application. You will rarely be asked purely theoretical questions without a connection to a dataset or a hypothetical field project. The interviewers are looking for a "researcher’s mindset"—someone who is methodical, detail-oriented, and capable of translating raw data into meaningful insights.

This timeline provides a high-level view of the progression from your initial application to the final rounds. Use this structure to pace your preparation: focus on technical skills early for the data task, and shift toward conceptual research methodology and behavioral alignment as you move into the interview rounds.

Microeconometric Tools

Your ability to use statistical software to perform causal inference is the cornerstone of this role. You will be evaluated on your mastery of regression analysis and your ability to diagnose model outputs.

Be ready to go over:

• Regression Analysis – Interpreting coefficients and identifying potential endogeneity.
• Balance Tests – Understanding why these are critical for validating randomization.
• Data Manipulation – Efficiently cleaning and preparing messy, real-world datasets.

Example scenarios:

• "Given this model output, explain the impact of the intervention on the treatment group."
• "How would you handle outliers in this specific dataset?"

Survey & Research Design

This area tests your ability to translate research goals into actionable field instruments. Good research starts with good design, and the lab places a high premium on candidates who understand this.

Be ready to go over:

• Survey Construction – Crafting questions that minimize response bias.
• Sampling Strategies – Choosing the right approach for specific worker populations.
• RCT Implementation – Identifying potential pitfalls in field experiments.

Example scenarios:

• "If you were designing a survey to measure worker well-being, what are the first three questions you would include?"
• "How do you account for non-response bias in a field survey?"

As a Research Analyst, your day-to-day will be a mix of deep-focus analysis and collaborative problem solving. You will spend a significant portion of your time working with large, often messy, datasets, cleaning them, and running econometric models to test the impact of various lab interventions.

You will work closely with other analysts and project leads to refine research designs, ensuring that every survey or experiment is optimized for high-quality data collection. You aren't just an observer; you are an active participant in the research lifecycle, from the initial design phase to the final presentation of findings to stakeholders.

A strong candidate for this position brings a solid foundation in economics or a related quantitative field. You must be able to demonstrate that you can work independently while keeping the broader research objectives in mind.

• Must-have skills: Proficiency in Stata or R, a strong grasp of microeconometrics, and experience with RCT methodology.
• Experience level: Familiarity with survey design and data cleaning is essential; you should have experience handling real-world, non-synthetic datasets.
• Soft skills: Clear communication of complex ideas, curiosity, and the ability to work within a team-oriented research environment.

Q: How long should I spend preparing for the data task? A: Treat the data task as a high-priority assignment. Ensure you are comfortable with data cleaning and visualization in your chosen software, as this is often the gatekeeper for the interview rounds.

Q: What is the most common reason candidates are not successful? A: Candidates often struggle when they can write the code but cannot explain the underlying research design or the "why" behind their methodological choices. Focus on the theory behind the tools.

Q: Is the work environment very academic? A: It is a hybrid. You are applying academic rigor to solve industrial problems, so you must be comfortable with the speed and constraints of the private sector while maintaining the discipline of a researcher.

• Structure your answers: When asked about research methodology, follow the "STAR" method (Situation, Task, Action, Result) to keep your explanations concise and focused.
• Show your process: If you are in a technical interview, talk through your thought process as you approach a problem. Interviewers want to see how you troubleshoot.
• Prepare for the "Why": Never just give a technical answer. Always explain why a certain method is appropriate for the specific research question at hand.
• Utilize resources: You can explore additional interview insights, practice questions, and preparation resources on Dataford to sharpen your skills.

The Research Analyst position at Good Business Lab is a unique opportunity to apply sophisticated analytical tools to challenges that have a tangible impact on the lives of workers. By mastering the core concepts of RCTs, refining your proficiency in Stata or R, and practicing how to articulate your research intuition, you will be well-positioned for success. Remember that your interviewers are looking for a teammate who is as rigorous in their thinking as they are collaborative in their work.

The salary data provided reflects current market ranges for research-oriented roles at this level in the sector. Use this as a benchmark for your own expectations, keeping in mind that total compensation may vary based on experience, specific location, and the unique structure of the role. Stay confident, prepare diligently, and best of luck with your application.