The Hershey Company Data Scientist Interview Questions + Guide in 2025

The Hershey Company is a global leader in the confectionery industry, known for its commitment to creating moments of Hershey happiness through its iconic products.

The Data Scientist role at Hershey involves applying advanced analytical techniques and scientific principles to interpret complex datasets and drive strategic business decisions. Key responsibilities include translating business problems into data science challenges, developing scalable algorithms, and proactively identifying innovative solutions using a variety of scientific methodologies. Candidates are expected to create metrics that quantify the impact of their work, produce clear technical documentation, and effectively communicate findings to non-technical stakeholders. Collaboration across multiple functions such as engineering, product management, and business is essential to ensure the alignment of data-driven insights with organizational goals.

The ideal candidate possesses deep expertise in machine learning, natural language processing, and other quantitative disciplines, along with proficiency in statistical software and programming languages like Python or R. A strong background in model development and implementation is critical, as is the ability to work with both experimental and observational data sets. An advanced degree in a quantitative field, coupled with relevant industry experience, will set candidates apart.

This guide will help you prepare for your interview by providing insights into the expectations for the role and equipping you with the knowledge to articulate your experiences and skills in alignment with Hershey's values and business strategies.

The interview process for a Data Scientist role at The Hershey Company is structured to assess both technical and interpersonal skills, ensuring candidates are well-rounded and fit for the collaborative environment. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, which usually takes place via a virtual call with a recruiter. This conversation lasts about 30-45 minutes and focuses on understanding your background, skills, and motivations for applying to Hershey. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, allowing you to gauge if it aligns with your career goals.

2. Technical Assessment

Following the initial screening, candidates may undergo a technical assessment. This could be a coding challenge or a take-home assignment that tests your data analysis skills, proficiency in programming languages (such as Python or R), and your ability to work with datasets. The assessment is designed to evaluate your problem-solving capabilities and your understanding of data science methodologies.

3. Behavioral Interviews

Candidates who pass the technical assessment will typically participate in one or more behavioral interviews. These interviews are conducted by team members and focus on your past experiences, how you approach problem-solving, and your ability to collaborate with cross-functional teams. Expect questions that explore your ability to translate business problems into data science solutions and how you communicate complex concepts to non-technical stakeholders.

4. Panel Interview

In some cases, candidates may be invited to a panel interview, which involves multiple interviewers from different departments. This stage assesses your fit within the team and the broader organization. You will be asked to discuss your previous projects, the impact of your work, and how you can contribute to Hershey's goals. This is also an opportunity for you to demonstrate your ability to influence and build consensus among diverse teams.

5. Final Interview

The final interview is often with senior leadership or key stakeholders. This stage is less about technical skills and more about strategic thinking and alignment with Hershey's vision. You may be asked to present a case study or a project you have worked on, showcasing your analytical skills and your ability to drive business impact through data science.

As you prepare for these interviews, it's essential to be ready to discuss your technical expertise and how it applies to real-world business challenges. Next, let's delve into the specific interview questions that candidates have encountered during the process.

Here are some tips to help you excel in your interview.

Emphasize Your Problem-Solving Skills

During the interview, be prepared to discuss how you approach complex business problems and translate them into data science challenges. Highlight specific examples from your past experiences where you identified innovative solutions or applied scientific methodologies to achieve impactful results. The interviewers at Hershey value candidates who can proactively identify opportunities and demonstrate a strong analytical mindset.

Showcase Your Communication Abilities

Given that the role involves collaborating with non-technical stakeholders, it’s crucial to demonstrate your ability to communicate complex concepts clearly and effectively. Prepare to explain your past projects in a way that highlights your thought process and the outcomes, ensuring that even those without a technical background can understand the significance of your work. This will help build trust and rapport with the interviewers.

Prepare for Behavioral Questions

Expect a significant focus on behavioral questions, as indicated by previous candidates' experiences. Reflect on your past experiences and be ready to discuss how you’ve contributed to team dynamics, handled challenges, and generated value in your previous roles. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process and the impact of your actions.

Familiarize Yourself with Hershey’s Culture

Understanding Hershey’s mission of creating the future of snacking can give you an edge. Research the company’s values and recent initiatives, especially those related to data science and analytics. Be prepared to discuss how your personal values align with Hershey’s and how you can contribute to their goals. This alignment can set you apart as a candidate who is not only technically proficient but also culturally fit.

Highlight Your Technical Proficiency

While the interview may lean towards behavioral questions, don’t neglect the technical aspects. Be ready to discuss your experience with data analytics, machine learning, and relevant software tools. Prepare to share specific projects where you utilized these skills, focusing on the methodologies you employed and the results achieved. This will demonstrate your technical competence and ability to apply it in real-world scenarios.

Practice Collaborative Scenarios

Since the role involves working with cross-functional teams, consider preparing for questions that assess your collaborative skills. Think of examples where you successfully worked with diverse teams, resolved conflicts, or influenced decision-making processes. Highlight your ability to harmonize different perspectives and lead discussions towards consensus, as this is crucial for influencing technical and business strategies at Hershey.

Be Authentic and Engaged

Finally, approach the interview with authenticity and enthusiasm. Show genuine interest in the role and the company, and don’t hesitate to ask insightful questions about the team dynamics, ongoing projects, or future challenges. This engagement not only reflects your interest but also allows you to assess if Hershey is the right fit for you.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically skilled but also a great cultural fit for The Hershey Company. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at The Hershey Company. The interview process will likely focus on your ability to analyze complex datasets, apply scientific principles, and communicate effectively with both technical and non-technical stakeholders. Be prepared to discuss your experience with machine learning, statistical analysis, and your approach to solving business problems.

Machine Learning

1. Can you describe a machine learning project you worked on and the impact it had on the business?

This question aims to assess your practical experience with machine learning and its application in a business context.

How to Answer

Discuss the project’s objectives, the methodologies you employed, and the results achieved. Highlight how your work contributed to business goals.

Example

“I worked on a customer segmentation project where we used clustering algorithms to identify distinct customer groups. This allowed the marketing team to tailor campaigns effectively, resulting in a 20% increase in engagement rates.”

2. How do you approach model validation and what techniques do you use?

This question evaluates your understanding of model validation processes and your ability to ensure model reliability.

How to Answer

Explain the importance of model validation and describe the techniques you use, such as cross-validation, A/B testing, or performance metrics.

Example

“I prioritize model validation by using k-fold cross-validation to ensure that my models generalize well to unseen data. I also track metrics like precision, recall, and F1 score to evaluate performance comprehensively.”

3. What is your experience with deploying machine learning models into production?

This question assesses your practical experience with the deployment phase of machine learning projects.

How to Answer

Discuss your experience with deployment tools and processes, including any challenges you faced and how you overcame them.

Example

“I have deployed models using Docker containers and integrated them with cloud services like AWS. One challenge was ensuring model performance in a production environment, which I addressed by implementing monitoring tools to track model drift.”

4. Can you explain the difference between supervised and unsupervised learning?

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Provide clear definitions and examples of both types of learning, emphasizing their applications.

Example

“Supervised learning involves training a model on labeled data, such as predicting sales based on historical data. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior.”

Statistics & Probability

1. How do you handle missing data in a dataset?

This question evaluates your data preprocessing skills and understanding of statistical methods.

How to Answer

Discuss various techniques for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I typically assess the extent of missing data first. For small amounts, I might use mean imputation, but for larger gaps, I prefer using predictive models to estimate missing values based on other features.”

2. What statistical methods do you use to analyze experimental data?

This question assesses your knowledge of statistical analysis techniques relevant to data science.

How to Answer

Mention specific statistical tests or methods you use, such as t-tests, ANOVA, or regression analysis, and explain when you would apply them.

Example

“I often use ANOVA when comparing means across multiple groups to determine if there are significant differences. For regression analysis, I apply it to understand relationships between variables and predict outcomes.”

3. Can you explain the concept of p-value and its significance?

This question tests your understanding of hypothesis testing and statistical significance.

How to Answer

Define p-value and explain its role in hypothesis testing, including how it influences decision-making.

Example

“A p-value indicates the probability of observing the data if the null hypothesis is true. A low p-value, typically below 0.05, suggests that we can reject the null hypothesis, indicating a statistically significant result.”

4. Describe a time when you used statistical analysis to solve a business problem.

This question assesses your ability to apply statistical methods in a real-world context.

How to Answer

Share a specific example, detailing the problem, the analysis performed, and the outcome.

Example

“I analyzed sales data to identify factors affecting seasonal fluctuations. By applying time series analysis, I uncovered trends that helped the sales team adjust inventory levels, reducing stockouts by 30% during peak seasons.”

Business Acumen

1. How do you translate business problems into data science problems?

This question evaluates your ability to connect business needs with data-driven solutions.

How to Answer

Discuss your approach to understanding business objectives and formulating data science questions that address those needs.

Example

“I start by collaborating with stakeholders to understand their challenges. For instance, when tasked with improving customer retention, I framed it as a classification problem to predict churn based on customer behavior data.”

2. Can you give an example of how your work has influenced business strategy?

This question assesses your impact on business decisions through data science.

How to Answer

Share a specific instance where your analysis or model directly influenced a strategic decision.

Example

“My analysis of customer feedback data led to the identification of key pain points in our product. Presenting these insights to leadership resulted in a strategic pivot that improved customer satisfaction scores by 15%.”

3. How do you communicate complex data findings to non-technical stakeholders?

This question evaluates your communication skills and ability to bridge the gap between technical and non-technical audiences.

How to Answer

Explain your strategies for simplifying complex concepts and ensuring clarity in your presentations.

Example

“I focus on storytelling with data, using visualizations to highlight key insights. I also avoid jargon and relate findings to business outcomes, ensuring stakeholders understand the implications of the data.”

4. What metrics do you consider most important when evaluating the success of a data science project?

This question assesses your understanding of project evaluation and success criteria.

How to Answer

Discuss the metrics you prioritize based on project goals, such as ROI, accuracy, or user engagement.

Example

“I prioritize metrics like ROI and user engagement, as they directly reflect the project’s impact on the business. For predictive models, I also track accuracy and precision to ensure reliability.”