Berkeley Research Group Research Analyst interview questions & guide 2026

The questions below are representative of what candidates face during the Berkeley Research Group interview process. They are drawn from real interview experiences and are meant to illustrate the patterns and themes of the evaluation, rather than serve as a memorization list.

Data Intuition and Methodology

These questions test your practical knowledge of handling data and your pre-analysis mindset. Interviewers want to see that you understand the mechanics of data cleaning and validation.

• Where would you go to find macroeconomic data, such as inflation or unemployment figures?
• What are the first three things you do when you receive a new, unfamiliar dataset?

Thorough preparation is essential for succeeding in the Berkeley Research Group interview process. The firm looks for candidates who possess a unique blend of technical data skills, economic intuition, and clear communication. You should approach your preparation by focusing on the following key evaluation criteria:

Analytical Rigor and Data Intuition In the context of Berkeley Research Group, this means understanding the "why" and "how" of data before you ever run a model. Interviewers will evaluate your ability to identify appropriate data sources, recognize potential flaws in a dataset, and ask critical questions about distributions and outliers. You can demonstrate strength here by clearly articulating your pre-analysis checklist and showing a healthy skepticism toward raw data.

Problem-Solving and Case Structuring This criterion measures how you break down ambiguous, real-world economic or business problems. Interviewers will look at your ability to structure a logical framework, identify the key variables, and propose a quantitative approach to find a solution. You can excel by practicing traditional consulting case studies, specifically those with a heavy emphasis on data analysis and market sizing.

Communication and Narrative Building Because your work will eventually be consumed by lawyers, judges, or corporate executives, you must be able to explain complex analytical concepts simply. You are evaluated on your clarity, conciseness, and ability to defend your methodological choices. Strong candidates treat the interview as a collaborative discussion, walking the interviewer through their thought process step-by-step.

Culture Fit and Adaptability Berkeley Research Group prides itself on a culture that is intellectually rigorous yet laid-back and extremely team-oriented. Interviewers want to see how you handle being thrown off guard by abstract questions and how you collaborate under pressure. You demonstrate this by remaining calm, thinking on your feet, and showing genuine enthusiasm for continuous learning.

The interview process for a Research Analyst at Berkeley Research Group typically blends behavioral discussions, technical data screening, and rigorous case interviews. While contract or part-time roles might involve an expedited, conversational phone screen leading directly to an offer, candidates applying for full-time roles (such as through campus recruiting) should expect a multi-stage, highly structured evaluation.

Your journey will generally begin with a first-round interview focused heavily on your past research experiences. Expect this conversation to dive deeply into your practical knowledge of data sourcing and your hands-on analytical methodology. The interviewer will want to know exactly how you approach a new dataset, step-by-step. If successful, you will be invited to a virtual Superday.

The Superday is an intensive, multi-hour process where you will meet with several team members, ranging from peers to senior consultants. This stage is a combination of case interviews, further deep-dives into your previous projects, and abstract behavioral questions designed to test your adaptability. Throughout the process, the atmosphere is generally described as painless and team-oriented, but the intellectual bar remains high.

This visual timeline outlines the typical progression from the initial resume screen through the final Superday rounds. You should use this to pace your preparation, focusing first on articulating your past research methodologies before shifting your energy to case study practice and abstract behavioral questions for the final rounds. Note that specific stages may vary slightly depending on whether you are applying for a specialized practice area or a generalist pool.

To succeed, you must understand exactly what the interviewers at Berkeley Research Group are trying to uncover during each phase of the conversation. Below is a detailed breakdown of the primary evaluation areas.

Data Sourcing and Pre-Analysis Methodology

Before you build a model, you must prove you know how to handle raw data. This area matters because litigation consulting requires bulletproof data integrity; a single unexamined outlier can ruin a multi-million dollar case. Interviewers want to see that you do not blindly trust datasets. Strong performance looks like having a structured, almost paranoid approach to data cleaning and exploration.

Be ready to go over:

• Public Data Sources – Knowing where to find reliable macroeconomic and demographic data.
• Data Exploration – Explaining how you assess the shape, size, and quality of a new dataset.
• Anomaly Detection – Your specific strategies for identifying and handling outliers and missing values.
• Advanced concepts (less common) – Imputation methods, handling heteroskedasticity, and specific statistical tests for data normality.

Example questions or scenarios:

• "Where would you go to find reliable data on inflation and employment rates?"
• "Walk me through the exact questions you ask yourself before diving into a new dataset."
• "How do you determine if an outlier should be removed or kept in your analysis?"

Case Interviews and Applied Problem Solving

Case interviews simulate the day-to-day ambiguity of consulting work. This area evaluates your ability to structure a problem, ask the right clarifying questions, and propose a quantitative solution. Strong candidates do not rush to an answer; they build a logical framework, state their assumptions clearly, and walk the interviewer through the math.

Be ready to go over:

• Market Sizing and Estimation – Using logic and basic proxies to estimate unknown figures.
• Economic Damages / Profitability – Structuring an approach to calculate lost profits or market share changes.
• Data-Driven Frameworks – Explaining exactly what data you would request from a client to solve a specific problem.

Example questions or scenarios:

• "A client claims their competitor's actions cost them 10% of their market share. How would you go about proving or disproving this?"
• "Estimate the total annual revenue of a specific regional airline."
• "If you were given a massive dataset of healthcare claims, how would you structure an analysis to find fraudulent billing?"

Past Research Experience and Behavioral Fit

Berkeley Research Group wants to know that you have successfully executed complex projects in the past and that you will thrive in their collaborative environment. This area is evaluated through deep-dives into your resume and abstract behavioral questions. Strong performance involves telling clear, structured stories (using the STAR method) and remaining composed when asked unconventional questions.

Be ready to go over:

• Project Deep-Dives – Explaining the objective, your specific role, the tools used, and the final impact of your past research.
• Overcoming Roadblocks – Discussing times when data was unavailable, messy, or contradictory.
• Abstract Thinking – Navigating unexpected, open-ended questions that test your personality and creativity.

Example questions or scenarios:

• "Walk me through the most complex research project on your resume from start to finish."
• "Tell me about a time you found a critical error in your own work. How did you handle it?"
• "If you could be anything in the world without worrying about salary or qualifications, what would you do?"

As a Research Analyst, your day-to-day work is heavily focused on data management, quantitative analysis, and quality control. You will spend a significant portion of your time identifying, acquiring, and cleaning large datasets from public sources (like FRED or the BLS) and proprietary client databases. You are responsible for ensuring that the data is accurate, properly formatted, and ready for advanced modeling.

Beyond data cleaning, you will conduct statistical and economic analyses using tools like R, Python, Stata, or advanced Excel. You will build dynamic financial models, run regressions, and perform sensitivity analyses to test the robustness of your findings. This requires a meticulous attention to detail, as your work will be audited by both your internal team and external opposing experts.

You will also be responsible for translating your analytical findings into compelling narratives. This involves creating data visualizations, drafting sections of expert reports, and building exhibits for use in court or client presentations. You will collaborate constantly with senior consultants, managers, and academic experts, requiring you to communicate your progress, roadblocks, and insights proactively and clearly.

To be highly competitive for the Research Analyst position at Berkeley Research Group, you need a strong foundation in economics, statistics, or a related quantitative field, paired with practical data skills.

• Must-have skills – Proficiency in data manipulation and statistical analysis using tools like R, Python, or Stata. Advanced Excel skills are mandatory. You must have a strong grasp of fundamental statistics (distributions, regressions, significance testing) and a demonstrated ability to source and clean messy data.
• Experience level – Typically, candidates are recent graduates with a Bachelor's or Master's degree in Economics, Statistics, Mathematics, or Data Science. Prior internships in economic consulting, financial services, or academic research are highly expected.
• Soft skills – Exceptional attention to detail, strong written and verbal communication, and the ability to work collaboratively in a fast-paced, team-oriented environment. You must be comfortable defending your analytical choices.
• Nice-to-have skills – Familiarity with specific databases (Bloomberg, Capital IQ), experience with data visualization tools (Tableau, PowerBI), and a foundational understanding of the litigation lifecycle or antitrust economics.

Q: How difficult is the interview process for a Research Analyst? The difficulty is generally rated as average to difficult, depending on your familiarity with case interviews and data methodology. The technical bar is high, particularly regarding how you handle data distributions and outliers, but the interviewers are typically collaborative and want to see how you think, not just if you know the right answer.

Q: What differentiates a successful candidate from an average one? Successful candidates do not just know how to run a regression; they know why they are running it and what could go wrong. They show a healthy skepticism of raw data, can articulate their data-cleaning checklist clearly, and remain composed when faced with abstract or highly ambiguous case questions.

Q: What is the culture like at Berkeley Research Group? Candidates consistently describe the culture at Berkeley Research Group as laid-back yet extremely team-oriented and intellectually rigorous. There is a strong emphasis on collaboration, meaning interviewers are assessing whether they would enjoy working late nights on a high-stakes case with you.

Q: How much time should I spend preparing for the Superday? You should dedicate significant time to practicing case studies out loud and refining your stories about past research projects. Plan for at least two weeks of focused preparation, ensuring you can smoothly explain your technical methodology and handle unexpected behavioral questions without freezing.

• Master the Pre-Analysis Checklist: Interviewers explicitly look for candidates who think before they code. Memorize and be ready to discuss a structured approach to receiving new data: checking for nulls, understanding distributions, identifying outliers, and verifying data types.
• Know Your Data Sources: Be prepared to name-drop specific, reliable public data sources like FRED (Federal Reserve Economic Data) or the BLS (Bureau of Labor Statistics) when discussing macroeconomic research.

• Embrace the Abstract: Berkeley Research Group interviewers sometimes use abstract questions (e.g., "If you could be anything in the world...") to see how you react to the unexpected. Do not panic. Take a breath, smile, and give an honest, structured answer that highlights your personality.
• Think Out Loud During Cases: In case interviews, silence is your enemy. Walk the interviewer through your logic, state your assumptions clearly, and ask clarifying questions before committing to a mathematical path.

Securing a Research Analyst role at Berkeley Research Group places you at the intersection of data science, economics, and high-stakes legal strategy. It is a demanding but incredibly rewarding position that will rapidly accelerate your analytical capabilities and business acumen. By preparing thoroughly, you are setting yourself up to join a team of world-class experts solving some of the most complex problems in the corporate and regulatory landscape.

To succeed, focus your preparation on mastering the fundamentals of data intuition—specifically how to source, clean, and question raw data. Practice structuring ambiguous case studies, and refine the narratives around your past research experiences so you can deliver them clearly and confidently. Remember that the interviewers are looking for a collaborative, rigorous thinker who can handle the unexpected with grace.

This compensation data provides a baseline expectation for the Research Analyst role. Keep in mind that total compensation in economic consulting often includes an end-of-year performance bonus, and starting figures can vary based on your specific location, academic background, and prior internship experience.

You have the analytical foundation necessary to excel in this process. Take the time to practice your communication, lean into your data curiosity, and approach the interviews as a collaborative problem-solving session. For further practice and to explore more detailed interview insights, you can utilize resources on Dataford. Good luck—you are ready for this!