General Mills Data Scientist Interview Questions + Guide in 2025

General Mills is a global leader in the food industry dedicated to making food that the world loves while fostering a culture of innovation and collaboration.

The Data Scientist role at General Mills is centered around leveraging advanced analytical techniques to derive actionable insights that drive business performance and consumer engagement. As a member of the Performance and Consumer Analytics (PCA) team, you will engage in a variety of responsibilities, including developing and implementing statistical models, conducting thorough analyses of large datasets, and collaborating closely with business teams to address strategic questions. A successful candidate will possess a strong foundational knowledge in data science, including proficiency in programming languages such as Python and R, and a deep understanding of machine learning and statistical methods. Importantly, this role requires a blend of technical expertise and business acumen, as you will need to communicate complex findings in a clear and impactful manner to diverse stakeholders within the organization.

This guide aims to equip you with the insights and preparation necessary to excel in your interview for the Data Scientist position at General Mills, helping you to demonstrate both your technical capabilities and your alignment with the company’s values and mission.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at General Mills. The interview process will likely assess your technical skills, problem-solving abilities, and how well you can translate data insights into actionable business strategies. Be prepared to discuss your past projects, statistical methods, and how you approach data-driven decision-making.

Machine Learning

1. Can you explain the ROC AUC curve and its significance?

Understanding the ROC AUC curve is crucial for evaluating the performance of classification models.

How to Answer

Discuss the concept of true positive and false positive rates, and how the AUC provides a single measure of model performance across different thresholds.

Example

“The ROC AUC curve plots the true positive rate against the false positive rate at various threshold settings. AUC values range from 0 to 1, where 1 indicates a perfect model. It’s particularly useful for comparing models, as it summarizes the trade-off between sensitivity and specificity.”

2. How do you determine when to use precision versus recall?

This question assesses your understanding of model evaluation metrics.

How to Answer

Explain the contexts in which precision or recall is prioritized, depending on the business problem.

Example

“In scenarios where false positives are costly, such as fraud detection, I prioritize precision. Conversely, in medical diagnoses, where missing a positive case is critical, I focus on maximizing recall.”

3. Describe a machine learning project you have worked on. What challenges did you face?

This question allows you to showcase your practical experience.

How to Answer

Detail the project, your role, the challenges encountered, and how you overcame them.

Example

“I worked on a customer segmentation project using clustering algorithms. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. This improved the model's accuracy significantly.”

4. Explain logistic regression and how it works.

This question tests your foundational knowledge of machine learning algorithms.

How to Answer

Discuss the mechanics of logistic regression, including the logistic function and how it predicts probabilities.

Example

“Logistic regression models the probability of a binary outcome using the logistic function. It estimates coefficients for each feature, which are then used to calculate the odds of the target event occurring.”

5. What is your experience with feature engineering? Can you provide an example?

Feature engineering is critical for improving model performance.

How to Answer

Share specific techniques you’ve used and their impact on model outcomes.

Example

“In a sales forecasting project, I created features from date variables, such as day of the week and month, which helped capture seasonal trends and improved the model’s predictive power.”

Statistics & Probability

1. Describe a statistical model you are currently using.

This question assesses your practical application of statistical models.

How to Answer

Explain the model, its purpose, and how you implement it in your work.

Example

“I’m currently using a linear regression model to analyze the impact of marketing spend on sales. By examining the coefficients, I can quantify the return on investment for different channels.”

2. What is the difference between random and fixed effects in modeling?

This question tests your understanding of advanced statistical concepts.

How to Answer

Clarify the definitions and when to use each type of effect in your models.

Example

“Random effects account for variability across different groups in the data, while fixed effects control for variables that do not change over time. I use random effects when I want to generalize findings beyond the sample data.”

3. How do you handle outliers in your data?

This question evaluates your data cleaning and preprocessing skills.

How to Answer

Discuss methods for identifying and treating outliers.

Example

“I typically use the IQR method to identify outliers and then assess their impact on the model. Depending on the context, I may choose to remove them or apply transformations to minimize their influence.”

4. Can you explain hypothesis testing and its importance?

Understanding hypothesis testing is fundamental in data analysis.

How to Answer

Discuss the process of hypothesis testing and its role in decision-making.

Example

“Hypothesis testing allows us to make inferences about a population based on sample data. It’s crucial for validating assumptions and guiding business decisions, such as determining the effectiveness of a new marketing strategy.”

5. What statistical software or tools do you prefer and why?

This question assesses your technical proficiency.

How to Answer

Mention the tools you are familiar with and their advantages.

Example

“I primarily use R for statistical analysis due to its extensive libraries for data manipulation and visualization. For larger datasets, I leverage Python’s Pandas and NumPy libraries for their efficiency and flexibility.”

Business Acumen

1. Why should General Mills hire you?

This question allows you to highlight your unique qualifications.

How to Answer

Focus on your skills, experiences, and how they align with the company’s goals.

Example

“I bring over seven years of experience in data science, particularly in the CPG sector. My ability to translate complex data into actionable insights aligns perfectly with General Mills’ mission to drive growth through analytics.”

2. Describe a time you turned data insights into actionable business strategies.

This question evaluates your ability to apply data analysis in a business context.

How to Answer

Share a specific example where your insights led to a significant business decision.

Example

“In a previous role, I analyzed consumer purchasing patterns and identified a gap in the market for a new product line. Presenting this data to the marketing team led to a successful launch that increased sales by 20%.”

3. How do you prioritize projects when working with multiple stakeholders?

This question assesses your project management and collaboration skills.

How to Answer

Discuss your approach to understanding stakeholder needs and prioritizing tasks.

Example

“I prioritize projects based on their potential impact on business objectives and stakeholder urgency. I maintain open communication with stakeholders to ensure alignment and adjust priorities as needed.”

4. How do you stay updated with the latest trends in data science?

This question evaluates your commitment to continuous learning.

How to Answer

Mention resources, communities, or courses you engage with to stay informed.

Example

“I regularly read industry blogs, participate in webinars, and attend conferences. I’m also part of several data science communities where I exchange knowledge and best practices with peers.”

5. Describe a difficult situation you encountered at work and how you resolved it.

This question assesses your problem-solving and interpersonal skills.

How to Answer

Share a specific challenge, your approach to resolving it, and the outcome.

Example

“I faced a situation where a key stakeholder was resistant to a data-driven recommendation. I organized a meeting to present the data in a more relatable way, which helped them understand the insights and ultimately led to their support for the initiative.”

Here are some tips to help you excel in your interview for the Data Scientist role at General Mills.

Emphasize Your Business Acumen

General Mills values candidates who can translate complex data insights into actionable business strategies. Be prepared to discuss how your previous projects have directly impacted business outcomes. Use specific examples to illustrate your ability to identify growth opportunities and how your analytical skills have contributed to strategic decisions. This will demonstrate your understanding of the business context in which data science operates.

Prepare for Diverse Interview Rounds

Expect a multi-stage interview process that includes technical, managerial, and HR rounds. Each round may focus on different aspects of your experience and skills. For the technical round, be ready to dive deep into your knowledge of machine learning, statistical analysis, and programming languages like Python and R. In the managerial round, focus on your collaboration and communication skills, as well as your ability to work in cross-functional teams. The HR round will likely assess your cultural fit, so be prepared to discuss your values and how they align with General Mills' mission.

Showcase Your Problem-Solving Skills

During the interview, you may encounter questions that require you to think critically and solve problems on the spot. Practice articulating your thought process clearly and logically. Use the STAR (Situation, Task, Action, Result) method to structure your responses, especially when discussing past projects or challenges you've faced. This will help interviewers understand your approach to problem-solving and your ability to deliver results.

Be Ready for Behavioral Questions

General Mills places a strong emphasis on collaboration and teamwork. Expect behavioral questions that explore how you've worked with others to achieve common goals. Prepare examples that highlight your ability to mentor colleagues, share knowledge, and foster a collaborative environment. This will demonstrate your alignment with the company's culture of growth and support.

Familiarize Yourself with Industry Trends

Stay updated on the latest trends in data science, particularly in the Consumer Packaged Goods (CPG) sector. Be prepared to discuss how these trends can be leveraged to drive business growth at General Mills. This knowledge will not only show your passion for the field but also your commitment to contributing to the company's success.

Engage with Your Interviewers

Interviews at General Mills are described as friendly and considerate. Use this to your advantage by engaging with your interviewers. Ask insightful questions about their experiences, the team dynamics, and the challenges they face. This will not only help you gauge if the company is the right fit for you but also demonstrate your genuine interest in the role and the organization.

Practice Your Technical Skills

Given the technical nature of the role, ensure you are comfortable with SQL, machine learning algorithms, and data manipulation techniques. Review common statistical concepts and be prepared to discuss how you have applied them in your work. Practicing coding challenges and data analysis problems can also help you feel more confident during the technical interview.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at General Mills. Good luck!

The interview process for a Data Scientist position at General Mills is structured to assess both technical expertise and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, each designed to evaluate different competencies and experiences relevant to the role.

1. Initial Screening

The process typically begins with an initial screening, which may be conducted via phone or video call. This interview is often led by a recruiter or a hiring manager and focuses on understanding the candidate's background, skills, and motivations for applying to General Mills. Expect questions about your resume, previous projects, and how your experience aligns with the company's goals.

2. Technical Interview

Following the initial screening, candidates usually participate in a technical interview. This round is designed to assess your analytical skills and proficiency in data science methodologies. Interviewers may ask you to solve problems related to statistical analysis, machine learning, and data manipulation using tools like Python, R, and SQL. Be prepared to discuss your past projects in detail, including the methodologies you employed and the outcomes achieved.

3. Managerial Interview

The next step often involves a managerial interview, where you will meet with a senior leader or manager from the team. This round focuses on your ability to collaborate with cross-functional teams and your understanding of business needs. Expect questions that explore your problem-solving approach, how you handle challenges in a business context, and your experience working in a team environment.

4. Behavioral Interview

In addition to technical skills, General Mills places a strong emphasis on cultural fit. A behavioral interview may be conducted to assess how well you align with the company's values and work culture. Interviewers will likely ask you to provide examples of past experiences that demonstrate your teamwork, leadership, and adaptability in various situations.

5. Case Study Presentation

Some candidates may be required to complete a case study as part of the interview process. This involves analyzing a specific business problem and presenting your findings and recommendations to the interview panel. This step allows you to showcase your analytical thinking, communication skills, and ability to translate data insights into actionable business strategies.

6. Final Interview

The final interview may involve meeting with upper management or a panel of interviewers. This round typically revisits key themes from previous interviews and may include a mix of technical and behavioral questions. It serves as a final opportunity for the interviewers to gauge your fit for the role and the organization.

Throughout the interview process, candidates should be prepared to discuss their technical skills, past experiences, and how they can contribute to General Mills' mission of driving business growth through data-driven insights.

Next, let's delve into the specific interview questions that candidates have encountered during this process.

1. What is the relationship between PCA and K-means clustering?

Explain how Principal Component Analysis (PCA) and K-means clustering can be used together in data analysis. Describe the benefits and potential drawbacks of combining these techniques.

2. Write a query to get the current salary for each employee.

Let’s say we have a table representing a company payroll schema.

Due to an ETL error, the employees table, instead of updating the salaries every year when doing compensation adjustments, did an insert instead. The head of HR still needs the current salary of each employee.

Write a query to get the current salary for each employee.

Note: Assume no duplicate combination of first and last names (I.E. No two John Smiths). Assume the INSERT operation works with ID autoincrement.

Example:

Input:

employees table

Column	Type
id	VARCHAR
first_name	VARCHAR
last_name	VARCHAR
salary	INTEGER
department_id	INTEGER

Output:

Column	Types
first_name	VARCHAR
last_name	VARCHAR
salary	INTEGER

Here are some quick tips on how you can prepare for your General Mills data scientist interview:

1. Know the Company Mission and Products: General Mills strongly emphasizes its mission to make food the world loves, so familiarity with its range of products and market could come in handy during situational and behavioral questions.

2. Be Ready with Your Projects: You should be prepared to discuss your past data science projects in depth, as previous project work is heavily scrutinized in General Mills interviews. Have a couple of diverse projects ready to talk about.

3. Brush Up on Your Technical Skills: Plan to review the basics in statistics and SQL, as well as machine learning concepts such as the ROC AUC curve, precision and recall, and logistic regression. Practicing on platforms like Interview Query can provide a great refresher.

What is the average salary for a Data Scientist at General Mills?

According to Glassdoor, data scientists at General Mills earn between $114K to $159K per year, with an average of $134K per year.

What qualities and skills is General Mills looking for in a Data Scientist?

General Mills looks for candidates with strong technical skills in data science, machine learning, and analytics. They value individuals with experience in applying these skills to drive business results and who can communicate their findings effectively to stakeholders at all levels. Collaboration, critical thinking, and creativity are highly valued traits.

What is the company culture like at General Mills?

General Mills’s culture prioritizes innovation, collaboration, and being a force for good. The company encourages big thinking and growth together, aiming to create a place where diverse perspectives and new possibilities flourish. General Mills also significantly emphasizes employee well-being, offering a competitive Total Rewards package.

The interview process at General Mills for the Data Scientist position offers invaluable insights into the company’s organized and thorough approach to candidate evaluation.

If you want more insights about the company, check out our main General Mills Interview Guide, where we’ve covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about General Mills’ interview process for different positions.

You can also check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!