Educational Testing Service (ETS) Data Scientist Interview Questions + Guide in 2025

Educational Testing Service (ETS) is a global leader in educational measurement and research, focused on advancing quality and equity in education through innovative assessment solutions and trusted research.

The Data Scientist role at ETS involves leveraging data-driven insights to inform strategic decisions and enhance product offerings. This position requires expertise in statistics, machine learning, and advanced data analysis techniques. Key responsibilities include developing predictive models, conducting A/B testing, and collaborating with cross-functional teams to optimize the customer experience and operational efficiency. Ideal candidates should possess strong analytical skills, proficiency in programming languages such as Python and SQL, and a passion for applying data science to solve complex educational challenges. Given ETS's commitment to quality and equity in education, a successful Data Scientist will align their work with the organization's mission, utilizing data to create impactful educational solutions.

This guide is designed to help you prepare for your interview by providing insights into the role's requirements and expectations, allowing you to showcase your relevant skills and experiences effectively.

The interview process for a Data Scientist role at ETS is designed to assess both technical expertise and cultural fit within the organization. It typically unfolds in several structured stages, allowing candidates to showcase their skills and engage with potential colleagues.

1. Initial Screening

The process begins with an initial screening, which usually involves a 30-minute phone interview with a recruiter. During this conversation, the recruiter will discuss the role, the company culture, and your background. This is an opportunity for you to express your interest in the position and to highlight relevant experiences that align with ETS's mission and values.

2. Technical Interview

Following the initial screening, candidates may participate in a technical interview. This round often includes a discussion with a data scientist or a technical lead, focusing on your proficiency in statistical analysis, machine learning, and programming languages such as Python and SQL. Expect to engage in problem-solving exercises that demonstrate your ability to analyze data and develop models, as well as discussions about your previous projects and how they relate to the work at ETS.

3. Behavioral Interview

Candidates will then typically face a behavioral interview, which may involve multiple interviewers, including team members and managers. This round assesses your interpersonal skills, teamwork, and alignment with ETS's values. You will be asked to provide specific examples from your past experiences that illustrate your problem-solving abilities, leadership qualities, and how you handle challenges in a collaborative environment.

4. Onsite Interview

The final stage is often an onsite interview, which can be a full-day event. This includes a series of interviews with various team members and stakeholders. You may be asked to present a job talk or discuss your previous research and its relevance to ETS's goals. This stage is crucial for both you and the interviewers to gauge fit within the team and the organization. Expect to engage in discussions about your approach to data-driven decision-making, metrics analysis, and how you would contribute to the ongoing projects at ETS.

Throughout the interview process, candidates should be prepared to discuss their technical skills in statistics, algorithms, and machine learning, as well as their experience in data visualization and cloud computing.

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.

Prepare for a Comprehensive Interview Process

The interview process at ETS can be extensive, often involving multiple rounds and interactions with various team members. Be ready for a full-day interview that may include a job talk and discussions with potential colleagues and managers. This format not only assesses your technical skills but also gives you a glimpse into the team dynamics and company culture. Prepare to articulate your past experiences and how they align with the role, as well as your future aspirations within the organization.

Showcase Your Technical Expertise

As a Data Scientist, you will be expected to demonstrate a strong command of statistics, algorithms, and programming languages like Python. Brush up on your knowledge of statistical modeling and machine learning techniques, as these are crucial for the role. Be prepared to discuss specific projects where you applied these skills, focusing on the methodologies you used and the outcomes achieved. Highlight your experience with data visualization tools and cloud computing services, as these are also important for the position.

Emphasize Collaboration and Communication Skills

ETS values teamwork and collaboration, so be ready to discuss how you have worked effectively in cross-functional teams. Share examples of how you have communicated complex data insights to non-technical stakeholders, as this will demonstrate your ability to bridge the gap between data science and business needs. Highlight your experience in mentoring or leading teams, as leadership is a key aspect of the role.

Align with ETS’s Mission and Values

Understanding and aligning with ETS’s mission to advance quality and equity in education is essential. Be prepared to discuss how your personal values and professional goals resonate with the company’s objectives. Show enthusiasm for contributing to educational solutions and how your work can impact learners globally. This alignment will not only demonstrate your fit for the role but also your commitment to the organization’s mission.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you faced significant challenges, how you approached them, and what you learned from those situations. This will help you convey your thought process and adaptability, which are crucial traits for a Data Scientist at ETS.

Ask Insightful Questions

At the end of your interview, you will likely have the opportunity to ask questions. Use this time to inquire about the team’s current projects, the company’s approach to data-driven decision-making, and how success is measured in the role. This not only shows your interest in the position but also helps you gauge if the company culture and expectations align with your career goals.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at ETS. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Educational Testing Service (ETS). The interview process will likely focus on your experience with data analysis, machine learning, statistical modeling, and your ability to communicate complex concepts effectively. Be prepared to discuss your past projects, your approach to problem-solving, and how you can contribute to ETS's mission of advancing quality and equity in education.

Experience and Background

1. Can you describe a project where you used data to drive decision-making?

This question aims to assess your practical experience in applying data science to real-world problems.

How to Answer

Discuss a specific project, detailing the problem you faced, the data you used, the analysis you conducted, and the impact of your findings on decision-making.

Example

“In my previous role, I worked on a project analyzing student performance data to identify factors affecting test scores. By employing regression analysis, I discovered that attendance had a significant impact. This insight led to the implementation of targeted interventions, resulting in a 15% improvement in scores over the next semester.”

Machine Learning

2. What machine learning algorithms are you most familiar with, and how have you applied them?

This question evaluates your technical knowledge and practical application of machine learning techniques.

How to Answer

Mention specific algorithms, explain their use cases, and provide examples of how you have implemented them in past projects.

Example

“I am well-versed in algorithms such as decision trees, random forests, and support vector machines. In a recent project, I used a random forest model to predict customer churn, which helped the marketing team develop targeted retention strategies that reduced churn by 20%.”

3. How do you approach feature selection in your models?

This question assesses your understanding of model optimization and data preprocessing.

How to Answer

Explain your process for selecting features, including any techniques or tools you use to evaluate their importance.

Example

“I typically use a combination of domain knowledge and statistical techniques like recursive feature elimination and correlation analysis to select features. For instance, in a predictive modeling project, I identified key features that significantly improved model accuracy by 30%.”

Statistics & Probability

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

This question tests your understanding of statistical concepts and their implications in data analysis.

How to Answer

Define both types of errors and provide context on their significance in hypothesis testing.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial, especially in educational assessments, where misclassifying a student's ability can have significant consequences.”

5. How do you handle missing data in your datasets?

This question evaluates your data cleaning and preprocessing skills.

How to Answer

Discuss the methods you use to address missing data, including imputation techniques or data removal strategies.

Example

“I often use multiple imputation techniques to handle missing data, as it allows me to maintain the dataset's integrity while providing a more accurate analysis. In one project, I used k-nearest neighbors imputation, which improved the model's predictive power by 25%.”

Data Visualization

6. What tools do you use for data visualization, and why?

This question assesses your ability to communicate data insights effectively.

How to Answer

Mention specific tools you are proficient in and explain why you prefer them for visualizing data.

Example

“I primarily use Tableau and Matplotlib for data visualization. Tableau allows for interactive dashboards that are great for stakeholder presentations, while Matplotlib is excellent for creating custom visualizations in Python scripts, which I find useful for exploratory data analysis.”

7. Can you provide an example of how you used data visualization to influence a decision?

This question looks for evidence of your ability to translate data into actionable insights.

How to Answer

Share a specific instance where your visualizations led to a significant decision or change.

Example

“In a project analyzing user engagement metrics, I created a dashboard that highlighted key trends in user behavior. This visualization prompted the product team to adjust their strategy, leading to a 30% increase in user retention over the next quarter.”

Collaboration and Communication

8. How do you ensure effective communication of complex data findings to non-technical stakeholders?

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

How to Answer

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

Example

“I focus on using clear visuals and relatable analogies to explain complex data findings. For instance, when presenting a model's results to the marketing team, I used a simple graph to illustrate the impact of our campaigns, which helped them understand the data without getting lost in technical jargon.”

9. Describe a time when you had to collaborate with a cross-functional team. What was your role?

This question assesses your teamwork and collaboration skills.

How to Answer

Provide an example of a project where you worked with different teams, highlighting your contributions and the outcome.

Example

“I collaborated with the product and marketing teams on a project to enhance user experience. My role involved analyzing user feedback data and presenting insights that informed design changes. This collaboration resulted in a 40% increase in user satisfaction ratings post-implementation.”