Prepare for and practice interview questions from Yum! Brands.

Yum! Brands Interview Questions

Yum! Brands Interview Guides

Machine Learning

Bagging vs Boosting

Regularization and Validation

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

Youtube Recommendations

Xgboost vs Random Forest

Behavioral

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

Describing a data project and its challenges

Hurdles In Data Projects

Prioritized debt reduction, process improvement, and a focus on maintainability for fintech efficiency

Decreasing Tech Debt

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Forecasting & Time Series

Significance Time Series

Why Do We Need Time Series Models?

Time Series Discrepancies

Business Case

Incorrect Packets

Demystifying data for non-technical users through visualization and clear communication

Accessible Data

Write a query to generate a shopping list that sums up the total mass of each grocery item required across three recipes.

Generate Shopping List from Recipes

Designing a dynamic sales dashboard to track McDonald's branch performance in real-time

Sales Leaderboard

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

Describe a data project you worked on. What were some of the challenges you faced?

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

What are the assumptions of linear regression?

Assumptions of Linear Regression

Statistics

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

What’s the relationship between PCA and K-means clustering?

What does the variable “k” in k-means clustering refer to?

What does the variable "k" in k-means clustering refer to?

Input of K-means

PCA and K-Means

How would you explain the bias variance tradeoff in machine learning to a high school student?

What are time series models? Why do we need them when we have less complicated regression models?

What can happen if a traditional regression model is used on data that has autocorrelation?

What can happen if a traditional regression model is used on data that has autocorrelation?

Why Do We Need Time Series Models? I

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why Do We Need Time Series Models? II

What are some effective ways to make data more accessible to non-technical people?

Let’s say that you’re working at a global trading company and you’re analyzing the price of a particular asset over time. This dataset is super noisy, volatile, and you’re not guaranteed completely accurate data.

How would you analyze this data to ensure there aren’t any discrepancies?

Let’s say you have a time series dataset grouped monthly for the past five years.

How would you find out if the difference between this month and the previous month was significant or not?

Let’s say you’re tasked with building the YouTube video recommendation algorithm.

How would you design the recommendation system?

What are important factors to keep in mind when building the recommendation algorithm?

Suppose that you work at a fintech startup. Recently, management has raised the issue of the increased amount of developer hours needed to implement relatively simple features, citing tech debt as the primary cause. How would you go around decreasing tech debt and decreasing developer turnaround time?

Suppose that you work for McDonald’s. You are to create a dynamic real-time dashboard highlighting the sales of each branch.

How would you design the database and engineering system for this?

Given a string, write a function <code>recurring_char</code> to find its first recurring character. Return <code>None</code> if there is no recurring character.

Treat upper and lower case letters as distinct characters.

You may assume the input string includes no spaces.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>input = &#34;interviewquery&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>output = &#34;i&#34;
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>input = &#34;interv&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>output = None
</code></pre>

Given a string, write a function to find its first recurring character.

Recurring Character

Data Structures & Algorithms

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

Example:

Input:

<pre tabindex="0" class="chroma"><code>stock_prices = [10,5,20,32,25,12]
dts = [
 &#39;2019-01-01&#39;, 
 &#39;2019-01-02&#39;,
 &#39;2019-01-03&#39;,
 &#39;2019-01-04&#39;,
 &#39;2019-01-05&#39;,
 &#39;2019-01-06&#39;,
]

def max_profit(stock_prices,dts) -&gt; 27
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def max_profit(stock_prices, dts) -&gt; 
(27, &#39;2019-01-02&#39;, &#39;2019-01-04&#39;)
</code></pre>

Write a function to find the best days to buy and sell a stock and the profit you generate from the sale.

Buy or Sell

You work as a data scientist at Cars.com. The company is launching a new personalized car recommendation engine that must support both real-time and batch inference, adapt to changing user preferences, and enable frequent model updates via CI/CD pipelines.

How would you design the end-to-end architecture for this system, ensuring it can robustly detect and respond to data distribution shifts while maintaining seamless CI/CD for machine learning models?

Design an end-to-end ML system for personalized car recommendations at Cars.com, supporting real-time and batch inference, data distribution shift detection, and CI/CD for frequent model updates.

Car Recommendation Architecture

You are a chef preparing for an important event. You downloaded three recipes to make and imported each as a separate table: <code>recipe1</code>, <code>recipe2</code>, <code>recipe3</code> into your database. Each recipe requires some grocery items in various quantities.

Write a query that sums up the total mass of each grocery item required across all recipes.

Input:

<code>recipe1</code> table:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>grocery</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>mass</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>recipe2</code> table:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>grocery</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>mass</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>recipe3</code> table:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>grocery</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>mass</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>grocery</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>total_mass</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

A manager in a packet filling line is complaining that their machine is not functioning correctly. The machine weighs a group of packets at once and attempts to get 25 packets into a box.

He has been getting complaints from customers that the boxes contain amounts other than 25. How would you help out?

You work as a data scientist for a grocery store chain that has a mobile app. At the end of the checkout process on the app, users are presented with an up-sell carousel, a sliding display of items that users can scroll/swipe to view and add to their cart. Currently, the carousel only presents items from the store’s personal brand.

How would you determine whether the carousel should replace store-brand items with national-brand products of the same type?

How would you determine whether the carousel should replace store-brand items with national-brand products of the same type?

Upsell Carousel

Product Sense & Metrics

Let’s say you run a small store that’s required to keep at least 6 bottles of water on display at all times. Right now, you have 12 bottles in stock, and you typically sell 2 bottles per day.

How many bottles would you need to order today to ensure you always meet the display requirement for the next 14 days, assuming you don’t get any additional deliveries in that period?

How many water bottles should you order to maintain a minimum display stock for two weeks?

Water Bottle Inventory

Supply Chain

Challenge

Yum! Brands Salaries by Position

<div><h2>NRM Business Analyst</h2>Ready to make your mark at an iconic global powerhouse? Join Yum! Brands, the parent company behind KFC, Pizza Hut, Taco Bell, and Habit Burger & Grill - be a part of our reinvention for Greater Growth.We are a culture-driven company where innovation, collaboration, and passion fuel everything we do. We're seeking talented individuals to make a difference across our global business. Whether you're just starting out or stepping into leadership, you'll help shape our future by delivering results that matter and bringing your smart, heart, and courage to the table.If you're ready to grow with a company that values curiosity, high performance, and meaningful workjoin us. Let's build the future of Yum! together!Yum! Brands is seeking an NRM Business Analyst to support high impact Net Revenue Management (NRM) initiatives across global markets. This role plays a critical part in translating complex data into structured insights and actionable recommendations across pricing, value architecture, promotional strategy, menu optimization, and channel growth. As part of the Global NRM COE, the Business Analyst will support strategy development from problem definition through executive-ready deliverables. This role requires strong analytical rigor, structured problem-solving, advanced Excel capability, and the ability to connect data to hypothesis-driven commercial recommendations.$90,000 - $105,000 + bonus eligibility. This is the expected salary range for this position. Ultimately, in determining pay, we'll consider the successful candidate's location, experience, and other job-related factors.<h3>Responsibilities</h3>Support end-to-end NRM projects, including problem definition, disaggregating issue trees, identifying key analyses, and structuring workstreams.Conduct detailed data analyses including day-part analysis, menu architecture analysis, channel profitability analysis, cost of sales (COS%) and contribution margin analysis.Analyze pricing, mix, traffic, and transaction data to identify opportunities for SSSG and AUV growth.Perform data cleansing and validation across large datasets; slice and manipulate data using advanced Excel (pivot tables, lookups, scenario modeling).Decode and analyze consumer survey data and market research to inform KVI strategy and value perception insights.Develop clear, client-ready slides and data visualizations that translate analysis into structured insights and actionable recommendations within NRM frameworks.<h3>Qualifications</h3>Minimum Requirements:<ul><li>Bachelor's degree in Business, Finance, Economics, Engineering, or related analytical field.</li><li>At least 3 years of experience in management consulting, corporate strategy, analytics, finance, or related analytical roles.</li><li>At least 3 years of advanced Excel experience, including pivot tables, large dataset manipulation, scenario modeling, and structured analysis.</li><li>Experience building executive-ready PowerPoint presentations based on quantitative analysis.</li><li>Demonstrated experience working with structured problem-solving approaches (e.g., hypothesis-driven analysis, issue trees).</li></ul>Preferred Requirements:<ul><li>Experience in QSR, retail, consumer packaged goods, or multi-unit business environments.</li><li>Exposure to Revenue Growth Management (RGM), pricing strategy, menu engineering, or commercial analytics.</li><li>Experience analyzing consumer research or survey datasets.</li><li>Proficiency in data visualization tools (e.g., Tableau, Power BI) or SQL.</li></ul>Benefits:Employees (and their eligible family members) may enroll in the following types of insurance coverage: medical, dental, vision, legal, and accidental death and dismemberment, as well as FSA/HSA (depending on enrolled medical plan). Yum! also provides short-term disability, long-term disability, and life insurance. Employees may enroll in our 401(k) plan. Yum! provides 4 weeks of vacation, paid sick leave, 10 paid holidays, a floating day off and 2 paid days for volunteer time each calendar year.At Yum!, one of our core values is to Believe in ALL People. This means seeing the value in everyone and unlocking their full potential to be their best self. Yum! Brands, Inc. (including its subsidiaries Yum Restaurant Services Group, LLC ("YRSG") and Yum Connect, LLC ("Yum Digital and Technology") (collectively, "Yum") is proud to be an equal opportunity employer and is committed to equity, inclusion, and belonging for all dimensions of diversity. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other protected characteristic. Yum! is committed to working with and providing reasonable accommodation to applicants with disabilities or special needs.US Job Seekers/Employees - Click here to view the "Know Your Rights" poster and supplement and the Pay Transparency Policy Statement.This posting will be open for a minimum of two weeks. Applications will be accepted on an ongoing basis until a candidate is selected.Beware of fake job postings using Yum! and/or our brand logos -- KFC, Pizza Hut, Taco Bell, and Habit Burger & Grill -- on fraudulent sites. Yum! Brands only posts jobs on official career pages and never asks for money during onboarding. Avoid unsolicited contacts via Telegram, WhatsApp, or similar social apps.<h3>Job Info</h3>Job Identification: 4873Posting Date: 03/06/2026, 04:28 PMDegree Level: Bachelor's DegreeJob Schedule: Full timeLocations: Plano, TX, United States (Hybrid)</div>

<h5>Product Manager Menu & Pricing </h5>This is a hybrid role (2 - 3 days per week) in Louisville, KY or Plano, TX. The Product Manager Menu & Pricing is responsible for overseeing the strategy, development, and operational excellence of all menu and pricing data across digital and in-restaurant channels. This role ensures that menu authoring, publishing, and pricing integrations operate with high accuracy, speed, and reliability to support business needs and deliver a seamless customer experience. The position serves as the central owner of menu data workflows, partnering with cross-functional teams, vendors, and restaurant operators to drive continuous improvement, accelerate change requests, and build a scalable roadmap that supports future growth and innovation. <h5>Responsibilities </h5>Develop and maintain the product roadmap for menu data management, menu authoring workflows, publishing systems, and pricing integration across all channels. Define strategic enhancements to improve menu data integrity, pricing automation, and the overall efficiency of change processes. Oversee the end-to-end process for menu creation, updates, pricing changes, and publishing across digital platforms and in restaurant systems. Manage interfaces and integration workflows between menu systems and pricing strategy providers to ensure accuracy and reliability. Collaborate closely with Operations, BOH teams, Digital stakeholders, Franchise partners, and vendors to gather requirements and translate them into clear product needs. Document requirements through user stories, acceptance criteria, use cases, and data workflows. Serve as the subject matter expert for menu data governance, menu publishing processes, and system behaviors. Own the intake, prioritization, and execution of menu-related and pricing-related change requests, ensuring fast and reliable turnaround. Identify operational bottlenecks and implement process improvements to accelerate menu publishing and reduce errors. Work with Distribution, QA, and Deployment teams to plan, schedule, and sequence releases to minimize operational impact. Groom, maintain, and prioritize the product backlog to align with business goals and technical constraints. Facilitate Agile ceremonies, collaborate with development teams, and ensure adherence to agile best practices. Partner with engineering teams to ensure solutions integrate effectively with BOH systems, restaurant devices, and third-party partners. Coordinate with external vendors and solution providers to plan and manage system releases, upgrades, and enhancements. Align release timing and communication with business units and market needs. Support QA through test planning, scenario creation, menu data validation, and integration testing. Establish and maintain data governance practices to ensure accuracy, consistency, and reliability of menu and pricing data. Create consistent feedback loops with restaurants to gather insights on menu usability, data issues, and system performance. Analyze recurring operational issues and translate them into product improvements or process optimizations. Monitor data quality and operational KPIs to ensure reliable menu publishing and minimize discrepancies across channels. Drive improvements that reduce friction in digital ordering and enhance the customer experience related to menu and pricing accuracy. Deliver measurable improvements in the speed, quality, and reliability of menu data operations. <h5>Qualifications </h5>Bachelor's degree in Computer Information Systems, Business Administration, or a related field is required. 10+ years of experience in data management and strategy with strong collaboration and cross functional stakeholder management experience. 2-3 years of experience in a product management role. Experience in requirements intake, demonstrating an understanding of customer needs and business objectives. 3+ years of experience with restaurant/retail systems, with menu management and pricing integrations preferred. Excellent communication skills and the ability to build productive relationships with diverse teams. Experience with Agile methodologies is advantageous, with Agile certification being a plus. Proficiency in Agile tools and user story writing is essential. A proven track record of delivering measurable improvements in data operations and customer experience. Salary Range: $137,900 - $151,690 annually + bonus eligibility. This is the expected salary range for this position. Ultimately, in determining pay, we'll consider the successful candidate's location, experience, and other job-related factors. Benefits: Employees (and their eligible family members) may enroll in the following types of insurance coverage: medical, dental, vision, legal, and accidental death and dismemberment, as well as FSA/HSA (depending on enrolled medical plan). Yum! also provides short-term disability, long-term disability, and life insurance. Employees may enroll in our 401(k) plan. Yum! provides 4 weeks of vacation, paid sick leave, 10 paid holidays, a floating day off and 2 paid days for volunteer time each calendar year. At Yum!, one of our core values is to Believe in ALL People. This means seeing the value in everyone and unlocking their full potential to be their best self. YUM! Brands, Inc. (including its subsidiaries Yum Restaurant Services Group, LLC ("YRSG") and Yum Connect, LLC ("Yum Digital and Technology") (collectively, "Yum") is proud to be an equal opportunity employer and is committed to equity, inclusion, and belonging for all dimensions of diversity. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other protected characteristic. Yum! is committed to working with and providing reasonable accommodation to applicants with disabilities or special needs.

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

Yum! Brands Interview Questions

Yum! Brands Interview Guides