AEG Research Scientist Interview Questions + Guide in 2025

AEG is a leading global entertainment and sports company that strives to create memorable experiences through innovative strategies and cutting-edge research.

As a Research Scientist, you will play a pivotal role in advancing AEG's mission by conducting foundational research at the intersection of Artificial Intelligence and sports analytics. Your key responsibilities will include producing research focused on multi-agent systems and reinforcement learning, developing deep learning models using Python, and building SQL pipelines to process and load data from various sources. A successful candidate will possess strong analytical skills, familiarity with programming languages such as Python, and a keen interest in artificial intelligence concepts. Your ability to collaborate effectively with cross-functional teams and adapt to a dynamic work environment will be crucial in driving impactful results. This role embodies AEG's core values of integrity, curiosity, empathy, collaboration, and originality, making it essential for candidates to demonstrate a commitment to these principles.

This guide aims to equip you with insights and knowledge to prepare for your interview, helping you stand out as a candidate who not only understands the technical requirements of the role but also aligns with AEG's values and culture.

The interview process for a Research Scientist at Aeg is designed to assess both technical expertise and cultural fit within the organization. It typically consists of several structured rounds that evaluate your skills in research, programming, and collaboration.

1. Initial Screening

The process begins with an initial screening, which is usually a phone interview with a recruiter. This conversation focuses on your background, experiences, and motivations for applying to Aeg. The recruiter will also provide insights into the company culture and the specifics of the Research Scientist role, ensuring that you understand the expectations and responsibilities associated with the position.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This round is often conducted via video conferencing and involves discussions around your proficiency in programming languages such as Python and SQL, as well as your understanding of algorithms and data visualization techniques. You may be asked to solve coding problems or discuss your previous projects that demonstrate your technical capabilities and analytical thinking.

3. Behavioral Interview

The next step in the process is a behavioral interview, where you will engage in a more in-depth conversation with a hiring manager or team lead. This interview focuses on your past experiences, how you handle challenges, and your approach to teamwork and collaboration. Expect questions that explore how you would integrate into the existing team and contribute to ongoing projects, particularly in the context of the company's mission and values.

4. Final Interview

The final interview typically involves a panel of interviewers, including senior researchers and team members. This round may include a mix of technical and behavioral questions, as well as a presentation of your previous work or research. You may be asked to discuss your understanding of multiagent systems, reinforcement learning, and how these concepts can be applied to the field of sports analytics. This is also an opportunity for you to ask questions about the team dynamics and future projects.

As you prepare for these interviews, it's essential to be ready for the specific questions that may arise during the process.

In this section, we’ll review the various interview questions that might be asked during an interview for the Research Scientist role at Aeg. The interview will likely focus on your technical skills, problem-solving abilities, and how you can contribute to the team’s goals in the context of sports analytics and artificial intelligence. Be prepared to discuss your experiences and how they align with the company’s mission and values.

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial for this role, especially in the context of developing models.

How to Answer

Clearly define both terms and provide examples of when each would be used. Highlight your experience with these concepts in your projects.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting player performance based on historical statistics. In contrast, unsupervised learning deals with unlabeled data, identifying patterns or groupings, like clustering players based on their playing styles.”

2. Describe a project where you implemented a deep learning model. What challenges did you face?

This question assesses your practical experience with deep learning, which is essential for the role.

How to Answer

Discuss a specific project, the model you used, and the challenges you encountered, along with how you overcame them.

Example

“I developed a convolutional neural network to analyze video footage of games. One challenge was overfitting, which I addressed by implementing dropout layers and data augmentation techniques to improve the model's generalization.”

3. How would you approach building a data pipeline using Python and SQL?

This question tests your technical skills in data processing and integration.

How to Answer

Outline the steps you would take to design and implement a data pipeline, emphasizing your familiarity with both Python and SQL.

Example

“I would start by identifying the data sources and the required transformations. Using Python, I would write scripts to extract data from APIs and databases, then use SQL to clean and aggregate the data before loading it into a data warehouse for analysis.”

4. What is reinforcement learning, and how can it be applied in sports analytics?

This question gauges your understanding of advanced AI concepts relevant to the role.

How to Answer

Define reinforcement learning and provide a specific example of its application in sports.

Example

“Reinforcement learning is a type of machine learning where an agent learns to make decisions by receiving rewards or penalties. In sports analytics, it can be used to optimize player strategies during games by simulating various scenarios and learning the best actions to take.”

5. Can you discuss your experience with data visualization tools? Which do you prefer and why?

Data visualization is key in communicating insights effectively.

How to Answer

Mention specific tools you have used, your preferred choice, and the reasons for your preference.

Example

“I have experience with Tableau and Matplotlib. I prefer Tableau for its user-friendly interface and ability to create interactive dashboards, which are essential for presenting complex data insights to stakeholders.”

Problem-Solving and Adaptability

1. Describe a time when you had to adapt to a significant change in a project. How did you handle it?

This question assesses your adaptability in a dynamic environment.

How to Answer

Provide a specific example of a project change and how you successfully navigated it.

Example

“During a project, we had to switch our data source mid-way due to compatibility issues. I quickly researched alternative sources, adjusted our data pipeline, and communicated the changes to the team, ensuring we stayed on schedule.”

2. How do you prioritize tasks when working on multiple projects?

This question evaluates your time management skills.

How to Answer

Discuss your approach to prioritization and any tools or methods you use.

Example

“I prioritize tasks based on deadlines and project impact. I use project management tools like Trello to keep track of progress and ensure that I allocate time effectively to high-priority tasks.”

3. Can you give an example of how you used data to drive a decision?

This question looks for evidence of your analytical thinking and decision-making skills.

How to Answer

Share a specific instance where your data analysis influenced a decision.

Example

“In a previous internship, I analyzed player performance data to recommend changes in training regimens. My analysis showed that certain players were underperforming due to fatigue, leading to adjustments in their training schedules that improved overall team performance.”

4. How would you handle a disagreement with a team member regarding a research approach?

This question assesses your collaboration and conflict resolution skills.

How to Answer

Explain your approach to resolving conflicts while maintaining a collaborative environment.

Example

“I would first listen to my team member’s perspective to understand their reasoning. Then, I would present my viewpoint and suggest a compromise or a trial of both approaches to see which yields better results, fostering a collaborative atmosphere.”

5. If hired, how would you integrate into a team that has been established for a long time?

This question evaluates your interpersonal skills and ability to fit into the company culture.

How to Answer

Discuss your approach to building relationships and contributing positively to the team.

Example

“I would take the time to understand the team dynamics and build relationships through open communication. I believe in actively listening to my colleagues’ insights and experiences, which would help me integrate smoothly and contribute effectively to our shared goals.”