Starbucks Data Scientist Interview Questions + Guide in 2024

Starbucks, a global powerhouse in the coffee industry, excels not only in delivering high-quality beverages but also in fostering a sense of community and connection. Known for its innovative and supportive work culture, Starbucks continuously invests in exceptional talent to drive its mission forward.

Data Scientists at Starbucks focus on leveraging data science and analytics to support decision-making within the Global Supply Chain. Key responsibilities include building predictive models, collaborating with cross-functional teams, and effectively communicating insights to stakeholders. Required skills include proficiency in SQL, Python, and R, along with a strong background in data management and statistical modeling. A Master’s degree in a quantitative field is typically expected for this role.

In this guide, we will help you through the interview process, common Starbucks data scientist interview questions, and valuable tips to help you succeed. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following in a Starbucks data scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, a Starbucks Talent Acquisition Team recruiter will contact you to verify critical details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Starbucks data scientist hiring manager may be present during the screening round to answer your queries about the role and the company itself. They may also engage in surface-level technical and behavioral discussions.

This recruiter call typically takes about 30 minutes.

Initial HackerRank Assessment

If you pass the initial screening, you will receive a link to a HackerRank coding assessment. The assessment usually consists of questions in Python, SQL, and R. Commonly, there are three questions: one SQL, two Python, and an R question focusing on data manipulation.

Technical Virtual Interview

Successfully navigating the initial assessment invites you to an in-depth technical interview. This 1-hour interview involves discussing your technical skills and knowledge in data science. Questions may revolve around Starbucks’ data systems, machine learning (ML) methods, predictive modeling, and data analytics.

The technical evaluation may also include a take-home assignment using a Kaggle dataset or a similar data exercise. Depending on the information from other candidates, you might break down your plan via screen share and subsequently work on the assignment offline.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop at the Starbucks office. During this interview stage, there can be multiple rounds, each 45 minutes long, where you’ll meet with various team members. These rounds often mix technical questions with discussions about teamwork, Starbucks operations, and behavioral queries.

If you were previously assigned a take-home exercise, a presentation round might be included to evaluate your problem-solving approach and modeling accuracy.

Typically, interviews at Starbucks vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

1. Write a function max_profit to find the maximum profit from buying and selling stocks along with the respective dates.

Given a list of stock_prices in ascending order by datetime, and their respective dates in list dts, write a function max_profit that outputs the max profit by buying and selling at a specific interval and the start and end dates to buy and sell for max profit.

2. How does random forest generate the forest and why use it over logistic regression?

Explain the process of how random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

3. How would you justify using a neural network model and explain predictions to non-technical stakeholders?

Describe the business problem and why a neural network is suitable. Explain the complexity and benefits of the model. Use simple analogies and visual aids to make the predictions understandable to non-technical stakeholders.

4. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Explain how to interpret the coefficients of logistic regression, focusing on the meaning of coefficients for categorical and boolean variables. Discuss how these coefficients indicate the relationship between the variables and the outcome.

5. Which model would perform better for predicting Airbnb booking prices: linear regression or random forest regression?

Compare linear regression and random forest regression in the context of predicting Airbnb booking prices. Discuss factors like model complexity, ability to handle non-linear relationships, and performance metrics to determine which model would likely perform better.

6. What are the assumptions of linear regression?

List and explain the key assumptions of linear regression, such as linearity, independence, homoscedasticity, normality, and no multicollinearity. Discuss why these assumptions are important for the validity of the model.

7. What would you do if friend requests are down 10% on Facebook?

A product manager at Facebook informs you that friend requests have decreased by 10%. How would you approach diagnosing and addressing this issue?

8. How would you set up an A/B test for changes in a sign-up funnel?

A team wants to A/B test multiple changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

9. What metrics would you use to determine the value of each marketing channel?

Given all the different marketing channels and their respective costs at a company called Mode, which sells B2B analytics dashboards, what metrics would you use to assess the value of each channel?

10. How would you measure the success of a banner ad strategy for an online media company?

An online media company wants to experiment with adding web banners into the middle of its reading content to monetize effectively. How would you measure the success of this banner ad strategy?

11. How would you investigate a drop in posts per user on Facebook?

The posting tool on Facebook composer drops from 3% posts per user last month to 2.5% posts per user today. How would you investigate this decline? If the drop is specifically in photo posts, what additional steps would you take?

12. How would you address a packet-filling machine that inaccurately fills boxes with 25 packets?

A manager reports that a machine, which weighs and attempts to fill boxes with 25 packets, is malfunctioning. Customers have complained about receiving boxes with incorrect packet counts. How would you investigate and resolve this issue?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Starbucks data scientist interview include:

• Demonstrate Real-World Application: Starbucks values practical data science applications. Be ready to discuss how your projects positively impacted prior organizations.
• Prepare for Behavioral Questions: Demonstrate your ability to align with Starbucks’ guiding principles and discuss times where you put the customer first, worked effectively with a team, or adapted to change.
• Be Proactive and Patient: The hiring process can be lengthy and may involve several rounds of rescheduling. Practice patience, follow-up respectfully, and maintain a proactive attitude.

What is the average salary for a Data Scientist at Starbucks?

What technical skills are required for a Data Scientist at Starbucks?

Starbucks looks for candidates with demonstrated experience in recommender systems, statistics, and scripting languages such as Python and SQL. Familiarity with Deep Learning frameworks (e.g., TensorFlow/Keras, PyTorch), Big Data processing tools (e.g., Spark/PySpark), and cloud platforms (e.g., Azure, AWS) is preferred. Knowledge of ETL processes, data visualization, and the ability to handle complex data sets is also crucial.

What type of responsibilities can one expect in the Data Scientist role at Starbucks?

The role involves implementing and improving customer-facing recommender systems, developing real-time machine learning applications, analyzing customer behavior within digital platforms, and consulting with stakeholders to identify pain points. Communicating technical insights to business partners and leading data science projects from conceptualization to implementation are also key responsibilities.

What qualities does Starbucks value in candidates for the Data Scientist position?

Starbucks values candidates who put the customer first, collaborate well with others, lead courageously, and continuously seek improvement. Strong problem-solving skills, attention to detail, and the ability to communicate effectively with both technical and non-technical stakeholders are essential. Prior experience in related fields like retail, customer loyalty, marketing, or eCommerce is a plus.

Aspiring to join Starbucks as a Data Scientist? The journey might be lengthy and fraught with communication hiccups, but you can turn challenges into triumphs with the right preparation.

If you’re eager to excel in the interview process, check ou our main Starbucks Interview Guide. We’ve covered other possible Starbucks data scientist interview questions there, equipping you with the insights you need to stand out.

Good luck with your interview!