Oshkosh Corporation Data Scientist Interview Questions + Guide in 2025

Oshkosh Corporation is a leading global manufacturer of specialty trucks and access equipment, dedicated to building, serving, and protecting communities around the world.

As a Data Scientist at Oshkosh, you will play a critical role in analyzing and interpreting complex datasets to derive actionable insights that inform business strategies and drive operational improvements. Your responsibilities will include applying statistical techniques and machine learning algorithms to solve business problems, processing both structured and unstructured data, and collaborating closely with cross-functional teams such as data analysts, engineers, and business stakeholders. You will be expected to communicate your findings effectively to both technical and non-technical audiences, leveraging data visualization tools to present your analysis. The ideal candidate will possess strong programming skills in Python, experience with data science tools like Azure Databricks and Power BI, and a solid understanding of data modeling and database management. A successful Data Scientist at Oshkosh embodies a people-first mindset and thrives on innovation, contributing to a culture that supports team member growth and success.

This guide will help you prepare for your interview by providing insights into the role's expectations and the skills necessary for success at Oshkosh Corporation.

The interview process for a Data Scientist at Oshkosh Corporation is designed to assess both technical skills and cultural fit within the organization. It typically consists of multiple stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Phone Screen

The first step in the interview process is an initial phone screen, usually conducted by a recruiter or HR representative. This conversation lasts about 30 minutes and serves as an opportunity for the recruiter to gauge your interest in the role, discuss your background, and assess your fit for the company culture. Expect questions about your previous experiences, motivations for applying, and a brief overview of your technical skills.

2. Technical Interviews

Following the initial screen, candidates typically undergo a series of technical interviews. These may be conducted over the phone or via video conferencing. During these interviews, you will be asked to demonstrate your proficiency in statistical techniques, machine learning, and programming languages such as Python. You may also be presented with case studies or hypothetical scenarios to analyze and solve, showcasing your problem-solving abilities and analytical thinking.

3. Behavioral Interviews

In addition to technical assessments, behavioral interviews are a crucial part of the process. These interviews focus on your past experiences and how they align with Oshkosh's values and team dynamics. Expect questions that explore your teamwork, communication skills, and how you handle challenges in a collaborative environment. You may be asked to provide examples of projects you've worked on and how you contributed to their success.

4. In-Person Interview

The final stage often includes an in-person interview, where you will meet with multiple team members. This round is more comprehensive and may involve a presentation about yourself or a specific project you have worked on. The goal is to assess not only your technical skills but also how well you can communicate complex ideas to both technical and non-technical audiences. This is also an opportunity for you to ask questions about the team and the projects you would be involved in.

5. Final Assessment

In some cases, there may be a final assessment or a follow-up interview to clarify any outstanding questions from previous rounds. This could involve additional technical questions or discussions about your fit within the team and the company culture.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and collaborative experiences.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Oshkosh Corporation. The interview process will likely focus on your technical skills in data analysis, machine learning, and statistical modeling, as well as your ability to communicate insights effectively to both technical and non-technical audiences. Be prepared to discuss your past projects and how they relate to the responsibilities outlined in the job description.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like customer segmentation in marketing.”

2. Describe a machine learning project you have worked on. What was your role?

This question assesses your practical experience and ability to contribute to projects.

How to Answer

Detail your specific contributions, the challenges faced, and the outcomes of the project. Emphasize collaboration with team members and any innovative solutions you implemented.

Example

“I worked on a predictive maintenance project for industrial equipment. My role involved developing a machine learning model to predict failures based on historical sensor data. I collaborated with engineers to gather data and iteratively improved the model, which ultimately reduced downtime by 20%.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and optimization.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Explain how you would apply these methods in practice.

Example

“To prevent overfitting, I use cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 or L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model assessment.

How to Answer

Mention various metrics relevant to the type of model (e.g., accuracy, precision, recall, F1 score for classification; RMSE for regression) and explain when to use each.

Example

“I typically use accuracy and F1 score for classification tasks to balance precision and recall. For regression models, I prefer RMSE as it provides a clear measure of prediction error, allowing for better model comparison.”

5. Can you explain a time when you had to optimize a machine learning model?

This question assesses your problem-solving skills and technical expertise.

How to Answer

Describe the optimization process, the techniques you used, and the results achieved. Focus on your analytical approach and any tools or frameworks utilized.

Example

“In a project to predict customer churn, I optimized the model by tuning hyperparameters using grid search and feature selection techniques. This improved the model’s accuracy by 15%, leading to more targeted retention strategies.”

Statistics & Probability

1. What is the Central Limit Theorem and why is it important?

This question tests your foundational knowledge in statistics.

How to Answer

Explain the theorem and its implications for statistical inference, particularly in relation to sample means.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for hypothesis testing and confidence interval estimation, as it allows us to make inferences about population parameters.”

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

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I might use mean imputation for small amounts of missing data or apply more sophisticated methods like K-nearest neighbors imputation for larger gaps.”

3. Explain the difference between Type I and Type II errors.

This question assesses your understanding of hypothesis testing.

How to Answer

Define both types of errors and provide examples to illustrate their implications in decision-making.

Example

“A Type I error occurs when we reject a true null hypothesis, leading to a false positive. Conversely, a Type II error happens when we fail to reject a false null hypothesis, resulting in a false negative. Understanding these errors is vital for making informed decisions based on statistical tests.”

4. What is p-value and how do you interpret it?

This question gauges your grasp of statistical significance.

How to Answer

Define p-value and explain its role in hypothesis testing, including how to interpret different p-value thresholds.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A p-value less than 0.05 typically suggests statistical significance, leading us to reject the null hypothesis.”

5. Can you describe a statistical model you have built? What was the outcome?

This question assesses your practical experience with statistical modeling.

How to Answer

Detail the model-building process, the data used, and the results achieved. Highlight any insights gained from the analysis.

Example

“I built a logistic regression model to predict loan defaults based on borrower characteristics. The model achieved an accuracy of 85%, allowing the financial institution to refine its lending criteria and reduce default rates by 10%.”