Optiver Research Scientist Interview Questions + Guide in 2025

Optiver is a tech-driven trading firm and a leading global market maker, recognized for its innovative market-making strategies and commitment to enhancing market efficiency.

As a Research Scientist at Optiver, you will be pivotal in advancing the company's machine learning initiatives, which are crucial for optimizing trading strategies. The role involves conducting innovative research on deep learning models, particularly for price forecasting, and developing robust training and inference pipelines. You will work closely with cross-functional teams, including engineers and quantitative researchers, to enhance existing deep learning frameworks and contribute to high-impact projects that directly affect trading operations.

Key responsibilities include leveraging your expertise in building deep-learning models with frameworks such as PyTorch, JAX, or TensorFlow and performing computationally intensive research on large datasets. A strong understanding of trading systems will also be essential for success in this role. Ideal candidates will possess a PhD or equivalent industry experience in machine learning, alongside a proven track record in developing scalable machine learning solutions.

The guide will help you prepare effectively for your interview by equipping you with insights into the company culture, specific role expectations, and the types of technical and behavioral questions you may encounter. With a clear understanding of what Optiver seeks, you will be better positioned to showcase your suitability for the Research Scientist role.

The interview process for a Research Scientist at Optiver is designed to rigorously assess both technical and behavioral competencies, ensuring candidates are well-suited for the dynamic environment of a tech-driven trading firm. The process typically unfolds in several stages:

1. Online Assessment

The first step in the interview process is an online assessment that can last between 2 to 3 hours. This assessment is multifaceted, including coding challenges, mathematical problems, and cognitive tests. Candidates may encounter questions that test their programming skills, particularly in Python, as well as their ability to solve algorithmic and statistical problems. The assessment may also include game-based questions that evaluate logical reasoning and mental agility.

2. Recruiter Phone Screen

Following the online assessment, candidates usually participate in a phone interview with a recruiter. This conversation focuses on the candidate's background, motivations for applying to Optiver, and alignment with the company culture. Expect to discuss your resume in detail, including specific projects and experiences that highlight your qualifications for the role.

3. Technical Interview

Candidates who successfully pass the recruiter screen will move on to a technical interview. This round typically involves in-depth discussions about your technical expertise, particularly in machine learning frameworks such as PyTorch, JAX, or TensorFlow. You may be asked to solve coding problems live, discuss your approach to building scalable machine learning models, and demonstrate your understanding of deep learning concepts.

4. Behavioral Interview

In addition to technical skills, Optiver places a strong emphasis on cultural fit. The behavioral interview assesses how candidates handle challenges, work in teams, and align with the company's values. Expect questions that explore your past experiences, problem-solving approaches, and how you manage stress and conflict in a team setting.

5. Final Round Interviews

The final stage of the interview process may involve multiple interviews with various team members, including senior researchers and engineers. This round is often more comprehensive, covering both technical and behavioral aspects. Candidates may be asked to present their previous work, engage in problem-solving discussions, and participate in collaborative exercises that simulate real-world scenarios relevant to the role.

As you prepare for your interview, be ready to tackle a variety of questions that will test your technical knowledge and your ability to fit within the Optiver team.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Optiver typically involves multiple stages, starting with an online assessment, followed by technical interviews, and concluding with behavioral assessments. Familiarize yourself with this structure and prepare accordingly. Expect a mix of coding challenges, logic tests, and discussions about your past projects. Knowing what to expect can help you manage your time and energy effectively during the interview.

Prepare for Technical Challenges

As a Research Scientist, you will likely face rigorous technical assessments. Brush up on your programming skills, particularly in Python, and be prepared to solve problems related to deep learning frameworks like PyTorch, JAX, or TensorFlow. Practice coding problems on platforms like HackerRank or LeetCode, focusing on medium to hard difficulty levels. Additionally, be ready to discuss your approach to building scalable and robust training and inference pipelines.

Showcase Your Research Experience

Optiver values candidates with a strong research background. Be prepared to discuss your previous research projects in detail, especially those related to machine learning and data analysis. Highlight your contributions, methodologies, and any innovative solutions you developed. If you have publications or contributions to open-source projects, be sure to mention them, as they can set you apart from other candidates.

Emphasize Problem-Solving Skills

Expect to encounter Fermi problems and brain teasers during your interviews. These questions assess your analytical thinking and problem-solving abilities. Practice these types of questions to become comfortable with thinking on your feet. When answering, articulate your thought process clearly, as interviewers at Optiver are interested in how you approach problems, not just the final answer.

Align with Company Culture

Optiver prides itself on a collaborative and inclusive environment. During your interviews, express your enthusiasm for teamwork and your ability to work well with others. Be prepared to discuss how you handle feedback and conflict within a team setting. Demonstrating that you align with Optiver's values will help you stand out as a candidate who fits well within their culture.

Prepare Thoughtful Questions

At the end of your interviews, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in the role and the company. Ask about the team dynamics, ongoing projects, or how the company is leveraging machine learning in its trading strategies. Thoughtful questions can leave a lasting impression and show that you are genuinely interested in contributing to Optiver's mission.

Practice Mental Math and Logic

Given the emphasis on quantitative skills, practice mental math and logic puzzles to sharpen your abilities. Many candidates have noted the importance of quick calculations and logical reasoning in the interview process. Being able to perform well under time constraints will be crucial, so consider incorporating timed practice sessions into your preparation.

By following these tips and preparing thoroughly, you can approach your interview at Optiver with confidence and clarity. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Research Scientist role at Optiver. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your past experiences, technical knowledge, and how you approach complex problems.

Technical Skills

1. Can you explain the architecture of a deep learning model you have built?

This question aims to assess your understanding of deep learning frameworks and your practical experience in building models.

How to Answer

Discuss the specific architecture you used, the reasoning behind your choices, and any challenges you faced during implementation.

Example

“I developed a convolutional neural network for image classification using PyTorch. The architecture included several convolutional layers followed by pooling layers, which helped in reducing dimensionality while preserving important features. I faced challenges with overfitting, which I mitigated by implementing dropout layers and data augmentation techniques.”

2. How do you optimize the performance of a deep learning model?

This question evaluates your knowledge of model optimization techniques.

How to Answer

Mention specific techniques you have used, such as hyperparameter tuning, regularization methods, or using advanced optimizers.

Example

“To optimize the performance of my model, I employed grid search for hyperparameter tuning, which allowed me to systematically explore different configurations. Additionally, I used early stopping to prevent overfitting and adjusted the learning rate dynamically during training to improve convergence.”

3. Describe a time you encountered a performance bottleneck in your model. How did you address it?

This question assesses your problem-solving skills and ability to troubleshoot.

How to Answer

Provide a specific example, detailing the bottleneck, your analysis, and the steps you took to resolve it.

Example

“I noticed that my model was taking an excessive amount of time to train due to large dataset size. I implemented data parallelism using multiple GPUs, which significantly reduced training time. Additionally, I optimized the data loading process by using asynchronous data loaders.”

4. What is your experience with distributed training?

This question gauges your familiarity with scaling models across multiple machines.

How to Answer

Discuss any frameworks or techniques you have used for distributed training, and the challenges you faced.

Example

“I have experience using TensorFlow’s distributed training capabilities. I set up a multi-worker training environment, which allowed me to scale my model across several GPUs. One challenge I faced was ensuring data consistency across workers, which I resolved by implementing a robust data sharding strategy.”

5. Can you explain the differences between PyTorch and TensorFlow?

This question tests your knowledge of deep learning frameworks.

How to Answer

Highlight the key differences in terms of usability, flexibility, and performance.

Example

“PyTorch is known for its dynamic computation graph, which makes it more intuitive for debugging and experimentation. In contrast, TensorFlow’s static graph can lead to better optimization and performance in production environments. I prefer PyTorch for research due to its flexibility, but I also appreciate TensorFlow for deploying models at scale.”

Behavioral Questions

1. Why do you want to work at Optiver?

This question assesses your motivation and alignment with the company’s values.

How to Answer

Discuss what attracts you to Optiver, such as its culture, mission, or innovative projects.

Example

“I am drawn to Optiver’s commitment to leveraging technology for market improvement. The opportunity to work on cutting-edge machine learning projects in a collaborative environment aligns perfectly with my career goals and values.”

2. Describe a challenging project you worked on. What was your role?

This question evaluates your teamwork and problem-solving skills.

How to Answer

Provide a specific example, detailing your contributions and the outcome of the project.

Example

“I worked on a project to develop a predictive model for stock price movements. My role involved data preprocessing, feature engineering, and model selection. Despite facing challenges with data quality, we successfully built a model that improved prediction accuracy by 15%.”

3. How do you handle tight deadlines and pressure?

This question assesses your time management and stress management skills.

How to Answer

Share your strategies for prioritizing tasks and maintaining productivity under pressure.

Example

“I prioritize tasks by assessing their impact and urgency. During a recent project with a tight deadline, I broke down the work into manageable chunks and set daily goals. This approach helped me stay focused and deliver the project on time without compromising quality.”

4. Tell me about a time you had to learn a new technology quickly.

This question evaluates your adaptability and willingness to learn.

How to Answer

Provide an example of a situation where you successfully learned a new technology and applied it.

Example

“When I needed to implement a model using JAX, I dedicated time to online courses and documentation. Within a week, I was able to build a prototype that utilized JAX’s capabilities for automatic differentiation, which improved our model’s performance.”

5. How do you approach collaboration with team members from different backgrounds?

This question assesses your interpersonal skills and ability to work in diverse teams.

How to Answer

Discuss your approach to fostering collaboration and communication in a diverse team setting.

Example

“I believe in open communication and actively seek input from team members with different expertise. In a recent project, I organized regular check-ins to ensure everyone’s voice was heard, which led to innovative solutions and a stronger team dynamic.”