Success at WestRock requires a balanced approach. You must demonstrate high technical competency while proving you can function as a collaborative partner within the business.

Technical Fluency – You must be prepared to discuss the specific tools and languages you have used in past projects. Interviewers want to see that you understand the "how" and "why" behind your technical choices, not just that you know how to call a function.

Problem Structuring – When presented with a case or a past project, break it down logically. Start with the business objective, move to your data approach, and conclude with the measurable impact your solution delivered.

Communication and Collaboration – WestRock values cross-functional success. You will be evaluated on your ability to explain your technical process to team members who may not have a data science background.

The interview process at WestRock is structured to evaluate both your technical depth and your alignment with the company’s operational goals. Typically, you will begin with an initial screening, often conducted via phone, to verify your background and gauge your interest in the specific challenges faced by the team. Following this, you can expect a series of rounds that delve deeper into your past project work and technical proficiency.

The process is designed to be rigorous but fair, focusing on your past performance as a predictor of future success. You should expect to be interviewed by a mix of HR representatives and technical leads who are looking for a combination of hard skills and the ability to contribute to a collaborative culture.

This timeline provides a high-level view of the progression from initial screening to deeper technical assessments. Use this structure to pace your study schedule, ensuring you have ample time to review your past projects before the technical rounds begin. Note that the intensity of the technical assessment may increase significantly after the initial HR screen.

Project Portfolio and Application

Your past work is the primary indicator of your potential. You should be prepared to provide a "deep dive" into at least two or three of your most significant projects.

Be ready to go over:

• Project Lifecycle – From data collection to model deployment and maintenance.
• Tools and Technologies – The specific software packages, languages, and frameworks used.
• Impact – How your work improved efficiency or outcomes for the business.

Example scenarios:

• "Describe a time your model produced unexpected results and how you troubleshot the issue."
• "What trade-offs did you consider when selecting your model architecture for this project?"

As a Data Scientist, your primary responsibility is to drive value through data. You will spend a significant portion of your time preparing and analyzing data to support decision-making processes. This involves:

• Partnering with cross-functional teams to identify data-driven opportunities for efficiency gains.
• Designing and implementing statistical models to predict outcomes in manufacturing and supply chain contexts.
• Automating reporting and data pipelines to ensure stakeholders have access to reliable insights.
• Presenting complex analytical findings in a clear, concise manner to leadership and operational managers.

You will act as both a technical expert and a business consultant, bridging the gap between raw data and strategic organizational action.

A competitive candidate for this role possesses a blend of strong technical foundations and the soft skills necessary to thrive in a large, matrixed organization.

• Must-have skills: Proficiency in Python or R, strong experience with SQL, and a solid understanding of statistical modeling and machine learning algorithms.
• Experience level: Most successful candidates have a proven track record of delivering end-to-end data projects, typically ranging from 2+ years of professional experience.
• Soft skills: Clear communication, intellectual curiosity, and the ability to simplify complex technical concepts for non-technical stakeholders.
• Nice-to-have skills: Experience with cloud platforms (e.g., AWS, Azure), data visualization tools like Tableau or Power BI, and familiarity with industrial manufacturing data.

Q: How long does the interview process typically take? While it varies by team and location, the process is generally completed within a few weeks across three core stages. Stay in close contact with your recruiter for updates on your specific timeline.

Q: Is the technical interview focused on whiteboard coding or project discussion? Expect a hybrid approach. You will likely be asked to discuss your past projects in detail, but you should also be prepared for technical questions regarding software packages and data manipulation methodologies.

Q: What is the best way to stand out? The most successful candidates are those who can clearly link their technical skills to business outcomes. Focus on the impact your projects had on the company’s bottom line or operational efficiency.

• Own your story: Be ready to explain exactly why you chose certain tools or methodologies for your past projects.
• Be concise: When answering technical questions, start with a high-level summary before diving into the details.
• Prepare for the "Why": Don't just say what you did; explain why it was the right choice for that specific business problem.

The Data Scientist role at WestRock offers a unique opportunity to apply sophisticated analytical models to a massive, global industrial operation. By focusing on your past project experiences, refining your ability to explain technical choices, and demonstrating a clear understanding of the business impact of your work, you will be well-positioned to succeed.

Preparation is the most significant factor in your interview performance. Use this guide to structure your review, practice your communication, and build the confidence necessary to excel. You have the potential to make a meaningful impact at WestRock—start your preparation today.