Case studies are often the most challenging aspect of data science interview processes. They are crafted to resemble a company’s existing or previous projects, assessing a candidate’s ability to tackle prompts, convey their insights, and navigate obstacles.
To excel in data science case study interviews, practice is crucial. It will enable you to develop strategies for approaching case studies, asking the right questions to your interviewer, and providing responses that showcase your skills while adhering to time constraints.
The best way of doing this is by using a framework for answering case studies. For example, you could use the product metrics framework and the A/B testing framework to answer most case studies that come up in data science interviews.
There are four main types of data science case studies:
Oftentimes as an interviewee, you want to know the setting and format in which to expect the above questions to be asked. Unfortunately, this is company-specific: Some prefer real-time settings, where candidates actively work through a prompt after receiving it, while others offer some period of days (say, a week) before settling in for a presentation of your findings.
It is therefore important to have a system for answering these questions that will accommodate all possible formats, such that you are prepared for any set of circumstances (we provide such a framework below).
Case studies assess your thought process in answering data science questions. Specifically, interviewers want to see that you have the ability to think on your feet, and to work through real-world problems that likely do not have a right or wrong answer. Real-world case studies that are affecting businesses are not binary; there is no black-and-white, yes-or-no answer. This is why it is important that you can demonstrate decisiveness in your investigations, as well as show your capacity to consider impacts and topics from a variety of angles. Once you are in the role, you will be dealing directly with the ambiguity at the heart of decision-making.
Perhaps most importantly, case interviews assess your ability to effectively communicate your conclusions. On the job, data scientists exchange information across teams and divisions, so a significant part of the interviewer’s focus will be on how you process and explain your answer.
Quick tip: Because case questions in data science interviews tend to be product- and company-focused, it is extremely beneficial to research current projects and developments across different divisions, as these initiatives might end up as the case study topic.
There are four main steps to tackling case questions in Data Science interviews, regardless of the type: clarify, make assumptions, gather context, and provide data points and analysis.
Clarifying is used to gather more information. More often than not, these case studies are designed to be confusing and vague. There will be unorganized data intentionally supplemented with extraneous or omitted information, so it is the candidate’s responsibility to dig deeper, filter out bad information, and fill gaps. Interviewers will be observing how an applicant asks questions and reach their solution.
For example, with a product question, you might take into consideration:
When you have made sure that you have evaluated and understand the dataset, start investigating and discarding possible hypotheses. Developing insights on the product at this stage complements your ability to glean information from the dataset, and the exploration of your ideas is paramount to forming a successful hypothesis. You should be communicating your hypotheses with the interviewer, such that they can provide clarifying remarks on how the business views the product, and to help you discard unworkable lines of inquiry. If we continue to think about a product question, some important questions to evaluate and draw conclusions from include:
The goal of this is to reduce the scope of the problem at hand, and ask the interviewer questions upfront that allow you to tackle the meat of the problem instead of focusing on less consequential edge cases.
Now that a hypothesis is formed that has incorporated the dataset and an understanding of the business-related context, it is time to apply that knowledge in forming a solution. Remember, the hypothesis is simply a refined version of the problem that uses the data on hand as its basis to being solved. The solution you create can target this narrow problem, and you can have full faith that it is addressing the core of the case study question.
Keep in mind that there isn’t a single expected solution, and as such, there is a certain freedom here to determine the exact path for investigation.
Finally, providing data points and analysis in support of your solution involves choosing and prioritizing a main metric. As with all prior factors, this step must be tied back to the hypothesis and the main goal of the problem. From that foundation, it is important to trace through and analyze different examples– from the main metric–in order to validate the hypothesis.
Quick tip: Every case question tends to have multiple solutions. Therefore, you should absolutely consider and communicate any potential trade-offs of your chosen method. Be sure you are communicating the pros and cons of your approach.
Note: In some special cases, solutions will also be assessed on the ability to convey information in layman’s terms. Regardless of the structure, applicants should always be prepared to solve through the framework outlined above in order to answer the prompt.
There have been multiple articles and discussions conducted by interviewers behind the Data Science Case Study portion, and they all boil down success in case studies to one main factor: effective communication.
All the analysis in the world will not help if interviewees cannot verbally work through and highlight their thought process within the case study. Again, interviewers are keyed at this stage of the hiring process to look for well-developed “soft-skills” and problem-solving capabilities. Demonstrating those traits is key to succeeding in this round.
To this end, the best advice possible would be to practice actively going through example case studies, such as those available in the Interview Query questions bank. Exploring different topics with a friend in an interview-like setting with cold recall (no Googling in between!) will be uncomfortable and awkward, but it will also help reveal weaknesses in fleshing out the investigation.
Don’t worry if the first few times are terrible! Developing a rhythm will help with gaining self-confidence as you become better at assessing and learning through these sessions.
With product data science case questions, the interviewer wants to get an idea of your product sense intuition. Specifically, these questions assess your ability to identify which metrics should be proposed in order to understand a product.
Start by answering: What is the goal of the private story feature on Instagram? You can’t evaluate “success” without knowing what the initial objective of the product was, to begin with.
One specific goal of this feature would be to drive engagement. A private story could potentially increase interactions between users, and grow awareness of the feature.
Now, what types of metrics might you propose to assess user engagement? For a high-level overview, we could look at:
However, we would also want to further bucket our users to see the effect that Close Friends stories have on user engagement. By bucketing users by age, date joined, or another metric, we could see how engagement is affected within certain populations, giving us insight on success that could be lost if looking at the overall population.
More context: Netflix is offering a promotion where users can enroll in a 30-day free trial. After 30 days, customers will automatically be charged based on their selected package. How would you measure acquisition success, and what metrics would you propose to measure the success of the free trial?
One way we can frame the concept specifically to this problem is to think about controllable inputs, external drivers, and then the observable output. Start with the major goals of Netflix:
Looking at acquisition output metrics specifically, there are several top-level stats that we can look at, including:
With these conversion metrics, we would also want to bucket users by cohort. This would help us see the percentage of free users who were acquired, as well as retention by cohort.
Start by considering the key function of Facebook Groups. You could say that Groups are a way for users to connect with other users through a shared interest or real-life relationship. Therefore, the user’s goal is to experience a sense of community, which will also drive our business goal of increasing user engagement.
What general engagement metrics can we associate with this value? An objective metric like Groups monthly active users would help us see if Facebook Groups user base is increasing or decreasing. Plus, we could monitor metrics like posting, commenting, and sharing rates.
There are other products that Groups impact, however, specifically the Newsfeed. We need to consider Newsfeed quality and examine if updates from Groups clog up the content pipeline and if users prioritize those updates over other Newsfeed items. This evaluation will give us a better sense of if Groups actually contribute to higher engagement levels.
Note: Given engineering constraints, the new feature is impossible to A/B test before release.
When you approach case study questions, remember always to clarify any vague terms. In this case, “effectiveness” is very vague. To help you define that term, you would want first to consider what the goal is of adding a green dot to LinkedIn chat.
What assumptions can you make about the relationship between weekly active users and email open rates? With a case question like this, you would want to first answer that line of inquiry before proceeding.
Hint: Open rate can decrease when its numerator decreases (fewer people open emails) or its denominator increases (more emails are sent overall). Taking these two factors into account, what are some hypotheses we can make about our decrease in the open rate compared to our increase in weekly active users?
Data analytics case studies ask you to dive into analytics problems. Typically these questions ask you to examine metrics trade-offs or investigate changes in metrics. In addition to proposing metrics, you also have to write SQL queries to generate the metrics, which is why they are sometimes referred to as SQL case study questions.
In this DoorDash analytics case study take-home question you are provided with the following dataset:
With a dataset like this, there are numerous recommendations you can make. A good place to start is by thinking about the DoorDash marketplace, which includes drivers, riders and merchants. How could you analyze the data to increase revenue, driver/user retention and engagement in that marketplace?
This is a Twitter data science interview question, and let’s say you implemented this new feature using an A/B test. You are provided with two tables: events (which includes login, nologin and unsubscribe) and variants (which includes control or variant).
We are tasked with comparing multiple different variables at play here. There is the new notification system, along with its effect of creating more unsubscribes. We can also see how login rates compare for unsubscribes for each bucket of the A/B test.
Given that we want to measure two different changes, we know we have to use GROUP BY for the two variables: date and bucket variant. What comes next?
More context: You are provided with a table of user experiences representing each person’s past work experiences and timelines.
This question requires a bit of creative problem-solving to understand how we can prove or disprove the hypothesis. The hypothesis is that a data scientist that ends up switching jobs more often gets promoted faster.
Therefore, in analyzing this dataset, we can prove this hypothesis by separating the data scientists into specific segments on how often they jump in their careers.
For example, if we looked at the number of job switches for data scientists that have been in their field for five years, we could prove the hypothesis that the number of data science managers increased as the number of career jumps also rose.
More context: You are given a table with search results on Facebook, which includes query (search term), position (the search position), and rating (human rating from 1 to 5). Each row represents a single search and includes a column has_clicked that represents whether a user clicked or not.
This question requires us to formulaically do two things: create a metric that can analyze a problem that we face and then actually compute that metric.
Think about the data we want to display to prove or disprove the hypothesis. Our output metric is CTR (clickthrough rate). If CTR is high when search result ratings are high and CTR is low when the search result ratings are low, then our hypothesis is proven. However, if the opposite is true, CTR is low when the search result ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.
With that structure in mind, we can then look at the results split into different search rating buckets. If we measure the CTR for queries that all have results rated at 1 and then measure CTR for queries that have results rated at lower than 2, etc., we can measure to see if the increase in rating is correlated with an increase in CTR.
You’re working as a Data Scientist in a local grocery chain’s data science team. The business team has decided to allocate store floor space by product category (e.g., electronics, sports and travel, food and beverages). Help the team understand which product categories to prioritize as well as answering questions such as how customer demographics affect sales, and how each city’s sales per product category differs.
Check out our Data Analytics Learning Path.
Machine learning case questions assess your ability to build models to solve business problems. These questions can range from applying machine learning to solve a specific case scenario to assessing the validity of a hypothetical existing model. The modeling case study requires a candidate to evaluate and explain any certain part of the model building process.
Common machine learning case study problems like this are designed to explain how you would build a model. Many times this can be scoped down to specific parts of the model building process. Examining the example above, we could break it up into:
How would you evaluate the predictions of an Uber ETA model?
What features would you use to predict the Uber ETA for ride requests?
Our recommended framework breaks down a modeling and machine learning case study to individual steps in order to tackle each one thoroughly. In each full modeling case study, you will want to go over:
Additionally, the customer can approve or deny the transaction via text response.
Let’s start out by understanding what kind of model would need to be built. We know that since we are working with fraud, there has to be a case where either a fraudulent transaction is or is not present.
Hint: This problem is a binary classification problem. Given the problem scenario, what considerations do we have to think about when first building this model? What would the bank fraud data look like?
Additional questions. How would you test the model and measure its accuracy? Remember the equation for precision:
Because we can not have high TrueNegatives, recall should be high when assessing the model.
Start by answering this question: What are the main differences between linear regression and random forest?
Random forest regression is based on the ensemble machine learning technique of bagging. The two key concepts of random forests are:
Random forest regressions also discretize continuous variables, since they are based on decision trees and can split categorical and continuous variables.
Linear regression, on the other hand, is the standard regression technique in which relationships are modeled using a linear predictor function, the most common example represented as y = Ax + B.
Let’s see how each model is applicable to Airbnb’s bookings. One thing we need to do in the interview is to understand more context around the problem of predicting bookings. To do so, we need to understand which features are present in our dataset.
We can assume the dataset will have features like:
Which model would be the best fit for this feature set?
More context: You do not have access to the feature weights. Start by thinking about the problem like this: How would the problem change if we had ten, one thousand, or ten thousand applicants that had gone through the loan qualification program?
Pretend that we have three people: Alice, Bob, and Candace that have all applied for a loan. Simplifying the financial lending loan model, let us assume the only features are the total number of credit cards, the dollar amount of current debt, and credit age. Here is a scenario:
Alice: 10 credit cards, 5 years of credit age, $\$20K$ in debt - **Bob:** 10 credit cards, 5 years of credit age, $\$15K$ in debt
Candace: 10 credit cards, 5 years of credit age, $\$10K$ in debt If Candace is approved, we can logically point to the fact that Candace’s $\$10K$ in debt swung the model to approve her for a loan. How did we reason this out?
If the sample size analyzed was instead thousands of people who had the same number of credit cards and credit age with varying levels of debt, we could figure out the model’s average loan acceptance rate for each numerical amount of current debt. Then we could plot these on a graph to model the y-value (average loan acceptance) versus the x-value (dollar amount of current debt). These graphs are called partial dependence plots.
In data science interviews, business case study questions task you with addressing problems as they relate to the business. You might be asked about topics like estimation and calculation, as well as applying problem-solving to a larger case. One tip: Be sure to read up on the company’s products and ventures before your interview to expose yourself to possible topics.
More context: You know that the product costs $\$100$ per month, averages 10% in monthly churn, and the average customer stays for 3.5 months. Remember that lifetime value is defined by the prediction of the net revenue attributed to the entire future relationship with all customers averaged. Therefore, $\$100$ * 3.5 = $\$350$… But is it that simple?
Because this company is so new, our average customer length (3.5 months) is biased from the short possible length of time that anyone could have been a customer (one year maximum). How would you then model out LTV knowing the churn rate and product cost?
See the full solution for this Amazon business case question on YouTube:
This question has no correct answer and is rather designed to test your reasoning and communication skills related to product/business cases. First, start by stating your assumptions. What are the goals of this promotion? It is likely that the goal of the discount is to grow revenue and increase retention. A few other assumptions you might make include:
How would we be able to evaluate this pricing strategy? An A/B test between the control group (no discount) and test group (discount) would allow us to evaluate Long-term revenue vs average cost of the promotion. Using these two metrics how could we measure if the promotion is a good idea?
More context: Say you have access to all customer spending data. With this question, there are several approaches you can take. As your first step, think about the business reason for credit card partnerships: they help increase acquisition and customer retention.
One of the simplest solutions would be to sum all transactions grouped by merchants. This would identify the merchants who see the highest spending amounts. However, the one issue might be that some merchants have a high-spend value but low volume. How could we counteract this potential pitfall? Is the volume of transactions even an important factor in our credit card business? The more questions you ask, the more may spring to mind.
Say that Netflix is working on a deal to renew the streaming rights for a show like The Office, which has been on Netflix for one year. Your job is to value the benefit of keeping the show on Netflix.
Start by trying to understand the reasons why Netflix would want to renew the show. Netflix mainly has three goals for what their content should help achieve:
One solution to value the benefit would be to estimate a lower and upper bound to understand the percentage of users that would be affected by The Office being removed. You could then run these percentages against your known acquisition and retention rates.
Let’s say you work at Amazon. It’s nearing Black Friday, and you are tasked with determining which products should be put on sale. You have access to historical pricing and purchasing data from items that have been on sale before. How would you determine what products should go on sale to best maximize profit during Black Friday?
To start with this question, aggregate data from previous years for products that have been on sale during Black Friday or similar events. You can then compare elements such as historical sales volume, inventory levels, and profit margins.
This course is designed teach you everything you need to know about feature changes: