Corning Incorporated Data Scientist Interview Questions + Guide in 2025

Corning Incorporated is a global leader in materials science, dedicated to developing life-changing technologies that shape industries and improve the world.

As a Data Scientist at Corning, you will play a crucial role in driving data-driven insights that enhance business performance and decision-making. Your responsibilities will include performing hands-on data analysis, applying advanced statistical and machine learning techniques, and collaborating with cross-functional teams to develop actionable insights. You will be expected to manage projects end-to-end, from gathering requirements to delivering comprehensive analyses and presenting key findings to leadership. A successful candidate will possess strong expertise in statistics, a solid understanding of algorithms, proficiency in Python and SQL, and a passion for translating complex data into meaningful business outcomes. The role aligns with Corning’s commitment to innovation and excellence, ensuring that your work contributes to the company's mission of solving tough challenges and advancing life sciences.

This guide aims to equip you with the knowledge and confidence to navigate the interview process effectively, highlighting key areas of focus and potential questions that reflect the skills and values emphasized by Corning.

The interview process for a Data Scientist role at Corning Incorporated is structured and thorough, reflecting the company's commitment to finding the right talent for their analytics center of excellence. The process typically includes several stages designed to assess both technical skills and cultural fit.

1. Initial Screening

The first step in the interview process is an initial screening, which usually takes place over the phone. This call is conducted by a recruiter and lasts about 20-30 minutes. During this conversation, the recruiter will discuss your background, experience, and interest in the role. They will also assess your fit for the company culture and clarify any logistical details, such as availability and relocation.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a programming quiz or a take-home assignment that tests your proficiency in relevant skills such as Python, SQL, and statistical analysis. The technical assessment is designed to evaluate your ability to apply analytical methods to real-world problems, including data modeling and machine learning techniques.

3. Video Interview

Candidates who pass the technical assessment are typically invited to a video interview. This stage involves discussions with team members and managers, focusing on your previous projects, technical abilities, and how your experience aligns with the work done at Corning. Expect to answer questions about your analytical approach, problem-solving skills, and how you have collaborated with stakeholders in past roles.

4. Onsite Interview

The final stage of the interview process is an onsite interview, which may also be conducted in a hybrid format. This stage usually includes a presentation where you will showcase your past work and insights derived from your projects. Following the presentation, you will participate in a series of one-on-one interviews with various team members, including technical and managerial staff. These interviews will delve deeper into your technical knowledge, statistical methods, and machine learning applications, as well as behavioral questions to assess your teamwork and communication skills.

Throughout the process, candidates are encouraged to demonstrate their curiosity and ability to think critically about data-driven solutions.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Corning Incorporated. The interview process will likely focus on your technical skills, experience with data analysis, and ability to communicate insights effectively. Be prepared to discuss your past projects, methodologies, and how you can contribute to the company's goals.

Technical Skills

1. Describe a project where you applied machine learning techniques. What was the outcome?

This question assesses your practical experience with machine learning and your ability to deliver results.

How to Answer

Discuss the specific machine learning techniques you used, the problem you were solving, and the impact of your work on the project or organization.

Example

“In a recent project, I developed a predictive model using random forests to forecast sales for a new product line. By analyzing historical sales data and customer demographics, I was able to improve the accuracy of our forecasts by 20%, which helped the marketing team optimize their strategies.”

2. How do you approach data cleaning and preprocessing?

This question evaluates your understanding of the importance of data quality in analysis.

How to Answer

Explain your typical workflow for data cleaning, including any tools or techniques you use to handle missing values, outliers, and data normalization.

Example

“I start by assessing the dataset for missing values and outliers. I use Python libraries like Pandas for data manipulation, filling in missing values with the mean or median, and removing outliers based on z-scores. This ensures that the data is clean and ready for analysis.”

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

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as regression and classification tasks. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering algorithms such as K-means.”

4. What statistical methods do you commonly use in your analyses?

This question gauges your statistical knowledge and its application in data science.

How to Answer

Mention specific statistical techniques you are familiar with and how you have applied them in your work.

Example

“I frequently use regression analysis to understand relationships between variables and hypothesis testing to validate my findings. For instance, I applied logistic regression to predict customer churn based on various features, which helped the business implement targeted retention strategies.”

5. Describe your experience with SQL and how you use it in data analysis.

This question assesses your ability to work with databases and extract meaningful insights.

How to Answer

Discuss your proficiency with SQL and provide examples of complex queries you have written.

Example

“I have extensive experience with SQL, using it to extract and manipulate data from relational databases. For example, I wrote complex queries involving joins and subqueries to analyze customer behavior across different segments, which informed our marketing strategies.”

Problem-Solving and Analytical Thinking

1. Tell us about a time you faced a significant challenge in a project. How did you overcome it?

This question evaluates your problem-solving skills and resilience.

How to Answer

Describe the challenge, your thought process in addressing it, and the eventual outcome.

Example

“During a project, I encountered a significant data quality issue that threatened our timeline. I organized a team meeting to brainstorm solutions, and we decided to implement a more robust data validation process. This not only resolved the issue but also improved our data handling for future projects.”

2. How do you prioritize multiple projects with competing deadlines?

This question assesses your organizational skills and ability to manage time effectively.

How to Answer

Explain your approach to prioritization and any tools you use to keep track of tasks.

Example

“I prioritize projects based on their impact on business goals and deadlines. I use project management tools like Trello to track progress and ensure that I allocate time effectively. Regular check-ins with stakeholders also help me adjust priorities as needed.”

3. Describe a time when you had to present complex data findings to a non-technical audience.

This question tests your communication skills and ability to convey technical information clearly.

How to Answer

Discuss how you tailored your presentation to the audience and the techniques you used to simplify complex concepts.

Example

“I once presented a data analysis report to the marketing team, which included non-technical members. I focused on visualizations to illustrate key points and avoided jargon, explaining the implications of the data in business terms. This approach helped them understand the insights and make informed decisions.”

4. What methods do you use to validate your models?

This question evaluates your understanding of model evaluation and performance metrics.

How to Answer

Discuss the techniques you use for model validation, such as cross-validation or A/B testing.

Example

“I use k-fold cross-validation to assess the performance of my models, ensuring that they generalize well to unseen data. Additionally, I track metrics like precision, recall, and F1 score to evaluate classification models, which helps me fine-tune them for better accuracy.”

5. How do you stay current with advancements in data science and machine learning?

This question assesses your commitment to continuous learning and professional development.

How to Answer

Mention specific resources, courses, or communities you engage with to keep your skills up to date.

Example

“I regularly read industry blogs, participate in online courses on platforms like Coursera, and attend data science meetups. I also follow key influencers on social media to stay informed about the latest trends and technologies in the field.”