The hershey company ML Engineer Interview Guide

Getting ready for an ML Engineer interview at The Hershey Company? The Hershey Company ML Engineer interview process typically spans technical, business, and communication-focused question topics, and evaluates skills in areas like machine learning system design, data analysis, model deployment, and translating technical solutions for business impact. Interview prep is especially important for this role at The Hershey Company, where ML Engineers are expected to develop and deploy robust machine learning models that optimize business operations, enhance product innovation, and support data-driven decision-making across a variety of functions. Success in this role means not only demonstrating technical excellence but also articulating how your solutions drive measurable value in a consumer-focused, innovation-driven environment.

In preparing for the interview, you should:

• Understand the core skills necessary for ML Engineer positions at The Hershey Company.
• Gain insights into The Hershey Company’s ML Engineer interview structure and process.
• Practice real The Hershey Company ML Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the The Hershey Company ML Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

The Hershey Company, headquartered in Hershey, PA, is a global leader in the confectionery industry, renowned for its iconic chocolate, sweets, mints, and snacks. With over 18,000 employees and more than 80 brands—including Hershey’s, Reese’s, Hershey’s Kisses, Jolly Rancher, and Ice Breakers—the company generates over $7.4 billion in annual revenue. Hershey is dedicated to ethical and sustainable business practices while expanding its presence internationally and diversifying its product portfolio. As an ML Engineer at Hershey, you will contribute to innovative solutions that support operational excellence and enhance product offerings, aligning with the company’s mission to bring goodness to people worldwide.

As an ML Engineer at The Hershey Company, you are responsible for designing, developing, and deploying machine learning models to support and enhance business operations such as supply chain optimization, demand forecasting, and product quality analysis. You will collaborate with data scientists, IT professionals, and business stakeholders to identify opportunities for automation and data-driven insights. Typical tasks include data preprocessing, model training and evaluation, and integrating solutions into production environments. This role is key in leveraging advanced analytics to drive efficiency, innovation, and informed decision-making across the company’s various functions.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough review of your application and resume by the Hershey Company’s talent acquisition team. They look for demonstrated experience in machine learning, deep learning, model deployment, and data-driven problem solving, as well as familiarity with scalable ML systems and strong programming skills in Python or similar languages. Highlighting projects that showcase your ability to solve business problems with ML, experience with cloud platforms, and clear communication of technical concepts will help your application stand out.

2.2 Stage 2: Recruiter Screen

Next, you’ll typically have a 30-minute phone call with a recruiter. This conversation is designed to assess your background, motivation for applying to Hershey, and overall fit for the ML Engineer role. Expect to discuss your career trajectory, key technical skills, and why you want to work at Hershey. Preparation should focus on articulating your experience with ML lifecycle management, cross-functional collaboration, and your passion for leveraging AI in large-scale business environments.

2.3 Stage 3: Technical/Case/Skills Round

This stage usually involves one or more interviews with senior ML engineers or data scientists. You’ll be evaluated on your ability to design and implement machine learning models, solve applied ML problems, and demonstrate technical depth in areas such as neural networks, model evaluation, and scalable deployment. Expect scenario-based questions that may cover experimentation (like A/B testing), system design, data quality, and real-time model serving. You may also be asked to explain complex ML concepts in accessible terms or to walk through a recent ML project, highlighting challenges and tradeoffs. Practice clear communication, structured problem-solving, and coding proficiency.

2.4 Stage 4: Behavioral Interview

The behavioral round, often conducted by a hiring manager or cross-functional peer, focuses on your collaboration, adaptability, and communication skills. You’ll be asked about your experience working on interdisciplinary teams, overcoming hurdles in data projects, and making ML insights actionable for non-technical stakeholders. Be ready to share examples of how you’ve handled ambiguity, advocated for data-driven solutions, and contributed to a positive team culture. Emphasize your ability to demystify technical content and present insights tailored to diverse audiences.

2.5 Stage 5: Final/Onsite Round

The final stage typically consists of a series of virtual or onsite interviews with various stakeholders, including senior leadership, engineering peers, and product partners. This round may include a deep-dive technical interview, a business case study, and a presentation or whiteboard session where you explain your approach to a real-world ML problem relevant to Hershey’s business (such as optimizing supply chain processes or improving customer experience). You may also be asked to discuss the ethical and business implications of ML solutions and how you ensure robust, scalable, and fair deployments.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll enter the offer and negotiation phase with the recruiter. This discussion will cover compensation, benefits, start date, and any final questions about the role or team. Hershey is known for a collaborative approach here, and candidates are encouraged to express their expectations and clarify role responsibilities.

2.7 Average Timeline

The typical Hershey Company ML Engineer interview process takes approximately 3-5 weeks from application to offer. Fast-track candidates may move through in as little as 2-3 weeks, especially if their experience closely aligns with the role’s requirements, while the standard process generally involves about a week between each stage. Scheduling for technical and onsite rounds can vary based on candidate and interviewer availability.

Next, let’s dive into the types of interview questions you can expect throughout this process.

3.1 Machine Learning System Design & Modeling

Expect questions on designing, evaluating, and deploying machine learning models for real-world business scenarios. Focus on how you select features, choose algorithms, validate models, and balance technical constraints with business needs.

3.1.1 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Frame your answer around experimental design (e.g., A/B testing), business impact metrics (revenue, retention, cost), and possible confounding factors. Provide a plan for implementation and how you’d monitor results.

3.1.2 Building a model to predict if a driver on Uber will accept a ride request or not
Discuss feature engineering, model selection (classification), and evaluation metrics. Emphasize handling imbalanced data and validating performance in production.

3.1.3 Identify requirements for a machine learning model that predicts subway transit
Outline how you’d gather data, define the prediction target, and select algorithms. Address scalability, real-time constraints, and integration into existing systems.

3.1.4 How would you approach the business and technical implications of deploying a multi-modal generative AI tool for e-commerce content generation, and address its potential biases?
Explain your strategy for model evaluation, bias detection, and mitigation, as well as stakeholder alignment and monitoring post-deployment.

3.1.5 How would you design a robust and scalable deployment system for serving real-time model predictions via an API on AWS?
Describe architecture choices, scalability, monitoring, and failover strategies. Highlight best practices for CI/CD and security.

3.2 Data Engineering & Infrastructure

Questions in this category assess your ability to design, maintain, and optimize data systems that support machine learning workflows. Focus on scalability, data integrity, and integration.

3.2.1 Design a data warehouse for a new online retailer
Discuss schema design, ETL processes, and how to support analytical queries efficiently. Consider scalability and future extensibility.

3.2.2 How would you estimate the number of trucks needed for a same-day delivery service for premium coffee beans?
Detail your approach to forecasting, demand modeling, and optimizing logistics using data-driven methods.

3.2.3 Which metrics and visualizations would you prioritize for a CEO-facing dashboard during a major rider acquisition campaign?
Describe your strategy for selecting actionable KPIs, designing clear dashboards, and ensuring data reliability.

3.2.4 How to model merchant acquisition in a new market?
Explain your approach to predictive modeling, feature selection, and business impact analysis.

3.2.5 Write code to generate a sample from a multinomial distribution with keys
Summarize how you’d implement sampling logic and discuss practical use cases for multinomial distributions in ML.

3.3 Natural Language Processing & Recommendation Systems

These questions focus on your experience with NLP, recommender systems, and extracting insights from unstructured data. Highlight your approach to feature engineering, model evaluation, and real-world deployment.

3.3.1 Delivering an exceptional customer experience by focusing on key customer-centric parameters
Identify metrics for customer satisfaction and describe how ML models can optimize user experience.

3.3.2 How would you analyze how the feature is performing?
Explain how you’d use data analysis and ML to evaluate feature adoption and impact.

3.3.3 Designing an ML system to extract financial insights from market data for improved bank decision-making
Discuss integration of external APIs, data preprocessing, and modeling for actionable insights.

3.3.4 Generating personalized weekly music recommendations for users
Describe collaborative filtering, content-based methods, and evaluation metrics for recommender systems.

3.3.5 Demystifying data for non-technical users through visualization and clear communication
Explain your techniques for making complex models and insights understandable to a broad audience.

3.4 Deep Learning & Model Justification

These questions test your understanding of deep learning architectures and your ability to communicate their value and limitations.

3.4.1 Explain neural networks to a young audience so they can grasp the core concepts
Use analogies and simple language to break down neural networks; focus on intuition over technical jargon.

3.4.2 Justify the use of a neural network for a given problem compared to other ML approaches
Articulate the advantages and limitations of neural networks, considering data complexity and business needs.

3.4.3 Describe the Inception architecture and its benefits in deep learning
Summarize key components, improvements over previous models, and practical use cases.

3.4.4 How would you approach improving the quality of airline data?
Discuss data cleaning, validation, and how robust data supports deep learning model accuracy.

3.4.5 Making data-driven insights actionable for those without technical expertise
Show how you tailor explanations and visualizations for non-technical stakeholders to drive adoption.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Describe the business context, the data analysis you performed, and the impact of your recommendation. Example: I analyzed sales data to identify declining product lines and recommended a targeted promotion, resulting in a 15% increase in sales.

3.5.2 Describe a challenging data project and how you handled it.
Focus on the obstacles, your problem-solving approach, and the outcome. Example: I led a project to integrate disparate data sources, overcoming schema mismatches by building a robust ETL pipeline.

3.5.3 How do you handle unclear requirements or ambiguity?
Discuss your approach to clarifying goals, iterating with stakeholders, and documenting assumptions. Example: I set up regular check-ins and prototypes to ensure alignment when requirements were vague.

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Highlight your communication and collaboration skills, and how you incorporated feedback. Example: I organized a workshop to discuss alternative modeling strategies and integrated team suggestions into the final solution.

3.5.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Explain your prioritization framework and stakeholder management. Example: I used MoSCoW prioritization and clear changelogs to maintain project focus.

3.5.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Share how you communicated trade-offs and provided interim deliverables. Example: I proposed a phased delivery plan, ensuring key milestones were met while maintaining data quality.

3.5.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Discuss your persuasion tactics and use of evidence. Example: I presented a compelling analysis that highlighted cost savings, leading to adoption of my recommendation.

3.5.8 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Describe your time management strategies and use of tools for tracking progress. Example: I maintain a prioritized backlog and schedule regular reviews to adjust timelines as needed.

3.5.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Show your initiative and technical skills in building sustainable solutions. Example: I developed automated scripts to flag anomalies, reducing manual review time by 50%.

3.5.10 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Discuss accountability and corrective action. Example: I immediately notified stakeholders, corrected the analysis, and updated documentation to prevent future errors.

4.1 Company-specific tips:

Familiarize yourself with The Hershey Company’s core business operations, especially how machine learning can drive improvements in supply chain optimization, demand forecasting, and product quality. Understanding the unique challenges of the confectionery and consumer packaged goods industry will allow you to tailor your solutions and showcase relevant impact during interviews.

Research recent initiatives at Hershey involving digital transformation, automation, and data-driven decision-making. Be prepared to discuss how you can contribute to ongoing innovation, such as optimizing manufacturing processes, enhancing customer experience, or supporting sustainability efforts through advanced analytics and ML.

Demonstrate a clear understanding of Hershey’s values, including ethical business practices and sustainability. Prepare to address how you would ensure fairness, transparency, and responsibility in your machine learning solutions, especially when models influence large-scale business decisions or customer-facing products.

Practice articulating the business value of your technical work. Hershey places a premium on ML Engineers who can bridge the gap between data science and business outcomes, so be ready to explain how your models can deliver measurable improvements in efficiency, revenue, or customer satisfaction.

4.2 Role-specific tips:

Showcase your experience designing, developing, and deploying production-ready machine learning models. Highlight your proficiency in the full ML lifecycle, from data preprocessing and feature engineering to model evaluation, monitoring, and continuous improvement. Be ready to discuss how you have handled data quality issues and ensured robust model performance in past projects.

Demonstrate your ability to collaborate cross-functionally. Hershey’s ML Engineers work closely with data scientists, IT, and business stakeholders, so prepare examples of how you have communicated complex technical concepts to non-technical audiences and aligned ML solutions with business priorities.

Be prepared to tackle system design questions involving scalable ML infrastructure. Practice explaining how you would architect end-to-end pipelines for real-time or batch predictions, select appropriate cloud tools, and ensure reliable model deployment, monitoring, and retraining.

Brush up on deep learning concepts and be ready to justify when and why you would use neural networks or advanced architectures for specific business problems. Use clear, simple language to explain deep learning to interviewers with varying levels of technical expertise.

Expect scenario-based questions that assess your ability to design experiments, such as A/B tests, to measure the impact of ML-driven changes. Discuss how you select metrics, control for confounding factors, and interpret results to inform business decisions.

Prepare to address ethical considerations in ML, such as bias detection, fairness, and transparency. Be ready to talk about how you would monitor models post-deployment and mitigate potential risks, especially in consumer-facing applications.

Highlight your programming skills, especially in Python, and your familiarity with ML frameworks and cloud platforms. Be ready to walk through code snippets, discuss your approach to testing and automation, and demonstrate your ability to write clean, maintainable code for ML systems.

Lastly, practice communicating your thought process clearly and confidently. Walk through your approach step-by-step, justify your decisions, and adapt your explanations based on the interviewer’s background—demonstrating both technical depth and business acumen.

5.1 How hard is the Hershey Company ML Engineer interview?
The Hershey Company ML Engineer interview is considered challenging, especially for those who have not previously worked in consumer goods or business operations environments. The process tests your ability to design, deploy, and explain machine learning solutions that drive measurable business impact—such as supply chain optimization and product quality improvements. Candidates who can demonstrate strong technical skills alongside clear business acumen and communication excel in this interview.

5.2 How many interview rounds does The Hershey Company have for ML Engineer?
Typically, there are 5 to 6 interview rounds for the ML Engineer role at The Hershey Company. The process includes a recruiter screen, one or more technical/case interviews, a behavioral interview, and a final onsite or virtual panel with senior stakeholders. Each round is designed to evaluate both your technical proficiency and your ability to collaborate and communicate effectively.

5.3 Does The Hershey Company ask for take-home assignments for ML Engineer?
Take-home assignments are occasionally part of the process, especially for candidates who need to demonstrate practical skills in model design, coding, or data analysis. Assignments may involve building a small ML solution, analyzing a dataset, or preparing a business case presentation. The goal is to assess your approach to real-world problems and your ability to translate technical work into actionable insights.

5.4 What skills are required for the Hershey Company ML Engineer?
Key skills include expertise in machine learning algorithms, model deployment, data preprocessing, and evaluation. Strong programming abilities in Python and familiarity with ML frameworks (such as TensorFlow or PyTorch) are essential. Experience with cloud platforms, scalable ML infrastructure, and data engineering is highly valued. Additionally, the ability to communicate technical concepts to non-technical stakeholders and align solutions with business goals is critical.

5.5 How long does the Hershey Company ML Engineer hiring process take?
The typical timeline is 3 to 5 weeks from application to offer. This can vary depending on candidate and interviewer availability, as well as the complexity of the interview rounds. Fast-track candidates may complete the process in about 2 to 3 weeks, while most applicants should expect a week between each stage.

5.6 What types of questions are asked in the Hershey Company ML Engineer interview?
Expect a mix of technical, business, and behavioral questions. Technical questions cover machine learning system design, model selection, deployment strategies, and data engineering. Business-centric questions assess your ability to drive impact through ML solutions, such as optimizing supply chain or forecasting demand. Behavioral questions focus on collaboration, communication, handling ambiguity, and influencing stakeholders.

5.7 Does The Hershey Company give feedback after the ML Engineer interview?
The Hershey Company generally provides high-level feedback through recruiters, especially if you progress to later stages. Detailed technical feedback may be limited, but you can expect insights on your strengths and areas for improvement, particularly regarding business alignment and communication.

5.8 What is the acceptance rate for Hershey Company ML Engineer applicants?
While exact numbers are not public, the ML Engineer role at The Hershey Company is highly competitive. The estimated acceptance rate for qualified applicants is around 3-5%, reflecting the company’s selective approach and the importance of both technical and business skills for this position.

5.9 Does The Hershey Company hire remote ML Engineer positions?
Yes, The Hershey Company offers remote ML Engineer positions, though some roles may require occasional visits to headquarters or regional offices for collaboration and onboarding. Flexibility depends on the team’s needs and the nature of specific projects, with remote and hybrid arrangements increasingly common.

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Ready to ace your The Hershey Company ML Engineer interview? It’s not just about knowing the technical skills—you need to think like a Hershey ML Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at The Hershey Company and similar companies.

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