Case study questions are one of the most important, yet often overlooked, parts of quantitative research interviews, particularly in final rounds and take-home assignments. These problems are designed to mimic real-world research scenarios: they’re open-ended, messy, and ambiguous. The goal isn’t just to test your technical skills, but to evaluate how you reason through uncertainty, structure a solution, and communicate your thought process clearly.
The topic may be finance-related or something entirely unrelated as long as it involves data analysis and modeling. For example:
How would you test the hypothesis: "Stock prices tend to decline following CEO resignation announcements"?
How would you determine the optimal number of CitiBikes to allocate to each docking station in New York City?
You might be asked to explain your process verbally, walk through a Jupyter notebook, or deliver a short research write-up. In all cases, balancing statistical rigor with practical intuition is key. Often, your ability to think critically and transparently will matter more than the specific answer you arrive at.
This section provides a structured framework for tackling these problems so you can demonstrate the kind of rigorous, hypothesis-driven thinking that top firms are looking for.
This is the most important skill in case study interviews. Interviewers aren’t just evaluating your technical skills—they’re assessing how clearly and transparently you think. Articulate your reasoning step-by-step, even if the path seems obvious to you.
In non-coding interviews especially, be explicit when working with data. Clearly define the structure of the dataframe you’re imagining: the column names, the units, and what each row represents. For example, if you say, “I’m going to filter for rows where the price exceeds a threshold,” first state that the dataframe consists of time series data with columns like timestamp, price, and volume. This ensures that both you and the interviewer share the same mental model and prevents miscommunication about your assumptions.
Most quant firms value candidates who approach problems with the scientific method.
That means: Observation → Question → Hypothesis → Experiment → Conclusion → Iteration.
Before performing any action (e.g., testing a model), explain what you expect to happen, what assumptions you’re making, and what outcomes would support or refute your hypothesis. This habit demonstrates that you’re not just doing something because it "feels right"—you’re reasoning based on prior beliefs and updating systematically.
Even if the question is open-ended, your response shouldn’t be. Structure your thinking: break the problem down, follow a logical progression, and summarize frequently. This helps the interviewer follow your line of thought and makes it easier for them to jump in if needed. More guidance on this will be provided in the next section.
Treat case studies like collaborative working sessions. They’re designed to mimic real-life problem-solving with a teammate. Imagine you and your interviewer standing at a whiteboard together, tackling an open-ended question.
If you’re stuck, say so. Recap your progress: "Here’s what we’ve done so far, and here are a few directions I’m considering. Is there one you think makes more sense?" This shows self-awareness, humility, and the ability to collaborate—all of which are highly valued.
When weighing modeling options, say both. For example: "We could try logistic regression for interpretability or a random forest for capturing nonlinearities. The tradeoff is between transparency and potential predictive power—what do you think would be more appropriate given our goal?"
By verbalizing this, you demonstrate that you’re not only technically capable but also thoughtful and aware of the broader context.
A strong case study response follows a scientific, hypothesis-driven approach. Top quant firms value candidates who can clearly explain not just what they’re doing, but why they’re doing it—especially in ambiguous, real-world settings. Below is a structured framework I use when approaching case study questions.
The first step is always to ground yourself in the problem. Case study prompts are intentionally vague, so it’s your job to remove ambiguity through careful questioning.
Before jumping into any analysis, make sure you have a clear grasp of the problem. Consider:
What is the specific research question or hypothesis?
What is the precise goal? Are we trying to predict something, test a theory, identify relationships, or optimize a process?
What is the evaluation metric or success criterion? Is it accuracy, Sharpe ratio, explanatory power, MSE, etc.?
What data is available? What are its limitations? Consider factors like:
Granularity (e.g., daily vs. intraday)
Data quality (e.g., outliers, missing values)
Lookahead bias or survivorship bias
Timeframe and sample size
In quantitative research, the data often is the problem—so scrutinizing it early and thoroughly is crucial. Don't assume the data is clean or well-behaved. Part of demonstrating your strength as a researcher is knowing what questions to ask before doing any modeling.
Once the problem and available data are well-defined, the next step is to explore the data and start forming hypotheses. This phase is critical for developing intuition about which features might matter and how the underlying system behaves.
What features or variables are likely to be important, and why?
Think from first principles or apply domain knowledge. For example, if the task is to predict stock returns after news events, variables like trading volume, sentiment score, or prior volatility might be relevant. Ask yourself: What drives the outcome, and how might that manifest in the data?
Perform exploratory data analysis (EDA) to uncover patterns, anomalies, or structures.
Examine distributions, time series trends, and cross-sectional relationships. Look for:
Outliers or missing values
Correlations (linear and non-linear)
Structural breaks or regime shifts
Violations of common assumptions (e.g., non-normality, autocorrelation, heteroskedasticity)
What tools will you use for EDA?
Common techniques include:
Summary statistics (e.g., statistical moments)
Line charts, histograms, boxplots, and scatter plots for visual insights
Correlation matrices and PCA for dimensionality and multicollinearity
Feature importance scores from tree-based models (e.g., Random Forests) to detect non-linear relationships
If you're in a verbal (non-coding) interview, explain what steps you would take if you had code.
For example, you might say: “I’d first create histograms of the target variable and key features, then plot rolling means and standard deviations to check for non-stationarity, followed by calculating pairwise correlations and feature importances using a random forest to capture non-linear effects.”
Use EDA as a hypothesis-generating tool.
Don’t just explore for the sake of it—aim to form data-driven hypotheses about what’s driving behavior in your dataset. This will directly inform the modeling and testing you do later.
Visualize everything.
Well-designed plots can immediately reveal inconsistencies, trends, or modeling challenges. Visualizations are also powerful communication tools in live interviews, as they allow you to walk your interviewer through your thought process.
Once you’ve explored the dataset and formed initial hypotheses, it’s time to clean and structure the data for modeling. This step ensures that your analysis is not biased by technical artifacts and that your model is learning from meaningful signals rather than noise.
Handle missing values, outliers, and potential data leakage.
Impute or drop missing values depending on the context and frequency.
Winsorize or transform outliers as needed, especially in financial data where fat tails are common.
Check for lookahead bias or data leakage—especially critical in time series or trading scenarios.
Apply necessary transformations.
Normalize or standardize variables if required (especially for distance-based or regularized models).
Use log transforms for skewed variables like volume or market cap.
In time series settings, consider differencing or percentage changes to induce stationarity.
Ensure your dataset is modeling-ready.
Make sure each row is a well-defined observation (e.g., a timestamped signal, a stock-day tuple, etc.).
Verify that time-based dependencies are respected in training/test splits (e.g., avoid leaking future data into the past).
Perform feature engineering.
Create new variables that might help capture relationships the model can’t easily learn from raw inputs.
In finance, this could include moving averages, volatility windows, cross-sectional ranks, or lagged returns.
Use domain knowledge and EDA results to guide what features are worth creating.
In non-coding interviews, be explicit about your imagined data.
Clearly describe the structure of your dataset, including:
Column names (e.g., timestamp, price, volume, return_1d, sector)
What each row represents (e.g., “each row is a stock on a given trading day”)
Units, frequency, and ordering of data
For example:
“I’m imagining a time series dataframe with one row per stock per day. Columns include timestamp, ticker, close_price, volume, and a computed return_1d. I’d filter for rows where volume exceeds a threshold to remove illiquid names.”
This level of clarity ensures you and the interviewer are perfectly aligned, avoids miscommunication, and shows your attention to detail.
With a clear understanding of the data and problem, the next step is to build a simple, interpretable model or statistical test as a starting point. This baseline helps ground your analysis and serves as a benchmark for any future improvements.
Start simple.
Choose a basic method appropriate for the task—linear regression, logistic regression, or a standard hypothesis test (e.g., t-test, chi-squared test). Don’t over-engineer at this stage.
State the assumptions of the method.
Every model or test comes with assumptions. For example:
Linear regression assumes linearity, homoskedasticity, and no multicollinearity.
A t-test assumes normality and equal variance.
Make sure you explicitly state these and evaluate whether they hold in your context.
Assess whether the assumptions are likely to be violated.
If the assumptions don’t hold (e.g., heavy-tailed residuals, autocorrelated errors), consider robust alternatives or transformation techniques. Acknowledge any limitations of proceeding with the current method.
Define the setup clearly.
What is the input data? (e.g., features X)
What is the target variable? (e.g., next-day return, binary outcome)
How is the data split? (e.g., train/test split, time-based cutoff)
What is the loss function or evaluation metric? (e.g., MSE, classification accuracy, p-value, Sharpe ratio)
Set expectations.
Before running the model or test, articulate what outcome you expect and why. For example:
“If my hypothesis is correct, I expect a positive and significant coefficient on the news_sentiment variable in my regression.”
Evaluate trade-offs.
After running the model, consider:
What insights did it provide?
What are the strengths and weaknesses of this approach?
Is the model interpretable, robust, or prone to overfitting?
This simple baseline acts as a diagnostic tool and hypothesis check. Even if it doesn’t yield a strong result, it provides valuable insight and helps you reason about what to try next.
Once you've run your initial model or test, the next step is to critically assess the results. This isn't just about whether you got a "good" metric—it's about questioning why the results look the way they do and whether they would hold up in the real world.
What should we do if we see inflated significance scores?
High t-statistics or low p-values might look promising, but they could be a sign of:
Overfitting
Lookahead bias
Multiple hypothesis testing without correction
Be skeptical—investigate the source of the signal and confirm that it’s not driven by artifacts in the data.
Is there bias in the experimental setup?
Scrutinize your workflow. Did you unintentionally leak information from the future? Are your cross-validation splits appropriate for time series data? Did you use data that wouldn’t have been available at the decision point?
How would this perform out of sample? Is distribution shift a concern?
Always consider how the model will behave on future, unseen data. Financial and operational environments change—seasonality, macro trends, or structural breaks can render your model obsolete. Check for stationarity, regime dependence, or hidden time-based effects.
Scrutinize your own research process.
Show critical thinking. What assumptions did you make? Where could you have gone wrong? What would invalidate your conclusion? Reflecting honestly on the limitations of your approach signals maturity and scientific rigor.
This is your opportunity to prove that you’re not just mechanically building models—you’re thinking like a researcher who wants to deploy something real. Show that you're aware of the traps and are proactively trying to avoid them.
A simple baseline is a great starting point—but not the end of the road. The next step is to build on your initial approach by addressing its limitations and refining your methodology.
Identify weaknesses in the baseline model or test.
Was it too rigid? Did it underfit the data? Were important features left out? Think critically about where your initial approach fell short and what could improve it.
Refine your approach step by step.
Don’t jump straight to complex methods. Instead, iteratively introduce improvements:
Add interaction terms or non-linear features
Apply transformations that better capture signal
Explore feature selection or dimensionality reduction (e.g., PCA)
Introduce more advanced techniques when appropriate.
If the baseline model doesn’t capture the complexity of the data, consider more sophisticated tools:
Regularized models like Ridge, Lasso, or Elastic Net to handle multicollinearity and prevent overfitting
Tree-based models like Random Forests or Gradient Boosted Trees to capture non-linearities
Machine learning or deep learning methods, such as neural networks or ensemble models, for highly non-linear or high-dimensional problems
Feature engineering remains key.
More complex models often benefit even more from thoughtful, domain-informed feature construction. Keep refining features based on new insights from each iteration.
Explain how each step improves your model.
Don’t just say “I added a Random Forest”. Instead, say:
“The baseline model underfit due to non-linear relationships between variables. I introduced a Random Forest to capture these patterns, which led to improved out-of-sample performance and a reduction in residual variance.”
Interviewers want to see that you’re not just throwing models at the problem—they want to see why each iteration makes sense and how your thinking evolves with each step.
To wrap up a strong case study, demonstrate that you're thinking beyond the immediate scope. Interviewers want to see that you understand the broader context and can imagine how your analysis might evolve in a real-world research setting.
If you had more time or resources, how would you extend the project?
Suggest next steps that go beyond polishing your model—think creatively. Would you explore different time horizons? Include interaction effects? Compare performance across different regimes?
What additional data would help?
Propose relevant datasets that could improve the robustness or breadth of your analysis. Examples might include:
Alternative data (e.g., news, weather, foot traffic)
Firm-level fundamentals or macroeconomic indicators
Cross-sectional or industry-level context Be specific about how the new data could enhance signal quality or reduce noise.
Could this approach be deployed in a real trading or portfolio setting?
Discuss potential implementation details:
How would you turn this into a production signal or screen?
What frictions (e.g., transaction costs, latency, liquidity) would affect real-world performance?
How would you monitor or recalibrate the model over time?
Thinking about deployment shows that you're not just a good analyst—you’re someone who understands the full research-to-production pipeline.
How would you estimate a covariance matrix of returns?
How would you test the hypothesis: “Momentum exists in stocks”?
How would you test the hypothesis: “Stock prices tend to decline following CEO resignation announcements”?
How would you develop a multi-factor risk model for a global equity portfolio?
How would you create a risk model for a portfolio of equities, commodities, and currencies?
How would you develop a trading strategy based on stock price reactions to earnings surprises?
How would you design a strategy for trading M&A opportunities?
How would you design a macro strategy based on government data releases (e.g., GDP, unemployment)?
How would you identify and trade a “digital transformation” theme?
How would you use satellite imagery of parking lots to predict quarterly earnings surprises?
How would you combine 20 alpha signals to construct an optimal portfolio?
How would you construct a volatility forecasting model?
How would you determine the optimal number of CitiBikes to allocate to each docking station in New York City?
How would you forecast demand for ride-sharing services during major city events?
How would you model energy consumption patterns across residential neighborhoods?
How would you design a system to detect fraudulent activity in an e-commerce platform?
How would you optimize ad placement to maximize click-through rate on a content platform?
How would you predict patient hospital readmission risk based on historical health records?
How would you estimate the impact of weather on retail foot traffic?
How would you allocate limited COVID-19 vaccines across different regions to maximize coverage?
Examples
General Data Science
Cracking the Data Science Interview by Maverick Lin
Ace the Data Science Interview by Nick Singh, Kevin Hou
Data Science Case Studies by khanhnamle1994
Exploratory Data Analysis
Exploratory Data Analysis with Python Cookbook by Ayodele Oluleye
Look at top Kaggle submissions—many of them begin with extremely detailed, high-quality EDA sections.
Data Preparation & Wrangling
Best Practices in Data Cleaning by Jason Osborne
Data Cleaning by Ihab Ilyas, Xu Chu
Feature Engineering and Selection by Max Kuhn, Kjell Johnson
Feature Engineering for Machine Learning by Alice Zheng, Amanda Casari
Look at top Kaggle submissions—many of them contain extremely detailed, high-quality data preparation sections.