Rakuten Research Scientist Interview Questions + Guide in 2025

Rakuten is a global leader in internet services, offering a diverse array of over 70 services across e-commerce, FinTech, digital content, and communications, all unified under a unique ecosystem model that leverages data and AI.

The Research Scientist role at Rakuten is pivotal in advancing the company's research agenda, particularly in the fields of deep learning and generative AI. Key responsibilities include driving innovative research to enhance large language models, designing and conducting impactful experiments, and pursuing independent research agendas that align with Rakuten's strategic goals. A successful candidate will possess a strong track record in deep learning research, ideally with first-author publications in peer-reviewed AI conferences, and demonstrate proficiency in programming and executing large-scale AI experiments. The role also emphasizes collaboration within a globally distributed team and mentorship of junior researchers, reflecting Rakuten's commitment to fostering a diverse and innovative corporate culture.

This guide will equip you with the insights necessary to navigate the interview process with confidence and clarity, enhancing your chances of securing a position as a Research Scientist at Rakuten.

The interview process for a Research Scientist at Rakuten is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several stages designed to evaluate your research capabilities, problem-solving skills, and alignment with Rakuten's values.

1. Application and Resume Screening

The process begins with an application submission, followed by a thorough resume screening. Recruiters will evaluate your academic background, research experience, and any relevant publications to determine if you meet the minimum qualifications for the role. This initial step is crucial as it sets the stage for the subsequent assessments.

2. Online Coding Assessment

Candidates who pass the resume screening are invited to complete an online coding assessment. This test is often conducted through platforms like Codility and focuses on evaluating your programming skills and problem-solving abilities. You may be required to solve algorithmic challenges or demonstrate proficiency in relevant programming languages, particularly Python.

3. Pre-Recorded Video Interview

Following the coding assessment, candidates may be asked to participate in a pre-recorded video interview. This step allows you to respond to a series of questions about your background, research interests, and motivations for applying to Rakuten. It’s an opportunity to showcase your communication skills and articulate how your experience aligns with the company's goals.

4. Presentation of Past Work

Candidates who successfully navigate the previous stages are typically required to prepare a presentation detailing their past research and its relevance to Rakuten's objectives. This presentation is a critical component of the interview process, as it allows you to demonstrate your expertise, research methodologies, and how your work can contribute to Rakuten's initiatives in generative AI and large language models.

5. Technical Interviews

The next phase consists of one or more technical interviews, which may be conducted via video conference. These interviews often involve discussions with senior researchers or team leads who will assess your technical knowledge in areas such as machine learning, deep learning, and experimental design. Be prepared for questions that probe your understanding of theoretical concepts as well as practical applications.

6. Final Interview with Leadership

In some cases, candidates may have a final interview with leadership or a panel of interviewers. This stage is designed to evaluate your fit within the company culture and your alignment with Rakuten's core values. Expect questions that explore your long-term vision, collaborative spirit, and how you can contribute to the company's mission of driving innovation.

As you prepare for your interviews, consider the types of questions that may arise during this process.

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

Prepare a Strong Presentation

Given the emphasis on presentations during the interview process, it's crucial to prepare a compelling presentation that showcases your past work and its relevance to Rakuten's goals. Focus on how your research can contribute to their initiatives in generative AI and large language models. Tailor your content to highlight innovative solutions and potential applications within Rakuten's ecosystem. Be ready to discuss not just your findings, but also the methodologies and thought processes behind your work.

Understand Rakuten's Ecosystem

Familiarize yourself with Rakuten's diverse range of services and how they integrate data and AI into their operations. This knowledge will help you answer questions about how your research aligns with their business objectives. Be prepared to discuss specific departments and projects within Rakuten, as interviewers may ask about your understanding of their current initiatives. This demonstrates your genuine interest in the company and your proactive approach to understanding its operations.

Embrace the Company Culture

Rakuten values a culture of continuous improvement, professionalism, and customer satisfaction. Reflect on how your personal values align with these principles and be ready to discuss examples from your past experiences that illustrate your commitment to these ideals. Show enthusiasm for contributing to a collaborative environment and emphasize your willingness to mentor junior researchers, as this aligns with their focus on teamwork and knowledge sharing.

Anticipate Technical Questions

While some interviewers may not have deep technical knowledge, be prepared for questions related to machine learning theory and methodologies. Brush up on fundamental concepts and be ready to explain complex ideas in a clear and concise manner. This will not only demonstrate your expertise but also your ability to communicate effectively with colleagues who may not have the same technical background.

Be Ready for Cultural Fit Questions

Expect questions that assess your fit within Rakuten's unique culture. Prepare to discuss why you want to work at Rakuten specifically and how you can contribute to their mission of empowering people through innovation. Highlight your adaptability and willingness to engage with diverse teams, as this is crucial in a globally distributed research environment.

Follow Up Professionally

After your interviews, consider sending a follow-up email to express your gratitude for the opportunity and reiterate your interest in the role. This not only shows professionalism but also keeps you on the interviewers' radar. If you receive a rejection, don't hesitate to ask for feedback; this can provide valuable insights for future interviews.

By following these tips, you can position yourself as a strong candidate who not only possesses the technical skills required for the Research Scientist role but also aligns well with Rakuten's values and culture. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Research Scientist interview at Rakuten. The interview process will likely assess your technical expertise in machine learning, your ability to conduct research, and your understanding of how your work can contribute to Rakuten's mission and services. Be prepared to discuss your past research, present your findings, and demonstrate your problem-solving skills.

Machine Learning and AI

1. Can you explain the differences 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 types of learning, providing examples of algorithms used in each. Highlight the scenarios in which each method is applicable.

Example

“Supervised learning involves training a model on labeled data, where the algorithm learns to predict outcomes based on input features. For instance, regression and classification tasks fall under this category. In contrast, unsupervised learning deals with unlabeled data, where the model identifies patterns or groupings, such as clustering algorithms like K-means.”

2. What are some common techniques for improving the performance of a machine learning model?

This question assesses your knowledge of model optimization.

How to Answer

Mention techniques such as hyperparameter tuning, feature selection, and using ensemble methods. Provide a brief explanation of each.

Example

“To enhance model performance, I often employ hyperparameter tuning to find the optimal settings for algorithms. Additionally, I utilize feature selection to eliminate irrelevant features, which can reduce overfitting. Lastly, ensemble methods like Random Forest or Gradient Boosting can combine multiple models to improve accuracy.”

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

This question allows you to showcase your practical experience.

How to Answer

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

Example

“In a recent project, I developed a convolutional neural network for image classification. One challenge was overfitting due to limited data. I addressed this by implementing data augmentation techniques and dropout layers, which significantly improved the model's generalization.”

4. How do you approach the evaluation of a machine learning model?

This question tests your understanding of model assessment metrics.

How to Answer

Discuss various evaluation metrics relevant to the type of model you are working with, such as accuracy, precision, recall, and F1 score.

Example

“I evaluate machine learning models using metrics appropriate for the task. For classification problems, I focus on accuracy, precision, and recall to understand the model's performance. I also use confusion matrices to visualize the results and identify areas for improvement.”

5. What is your experience with large language models (LLMs)?

Given the focus on LLMs at Rakuten, this question is particularly relevant.

How to Answer

Share your experience with LLMs, including any specific projects or research you have conducted.

Example

“I have worked with large language models like BERT and GPT-3 for natural language processing tasks. In one project, I fine-tuned BERT for sentiment analysis, which involved preprocessing text data and optimizing the model for better accuracy on our specific dataset.”

Research and Development

1. Describe your research agenda and how it aligns with Rakuten's goals.

This question assesses your ability to connect your work with the company's mission.

How to Answer

Outline your research interests and how they can contribute to Rakuten's objectives, particularly in generative AI.

Example

“My research agenda focuses on developing generative models for personalized content creation, which aligns with Rakuten's goal of enhancing user engagement. By leveraging user data, I aim to create models that generate tailored recommendations, improving customer satisfaction.”

2. How do you stay updated with the latest advancements in AI and machine learning?

This question evaluates your commitment to continuous learning.

How to Answer

Mention specific journals, conferences, or online platforms you follow to keep abreast of new developments.

Example

“I regularly read publications from conferences like NeurIPS and ICML, and I follow leading researchers on platforms like Twitter and LinkedIn. Additionally, I participate in online courses and webinars to deepen my understanding of emerging technologies.”

3. Can you discuss a time when you collaborated with a team on a research project?

Collaboration is key in research roles, and this question assesses your teamwork skills.

How to Answer

Describe the project, your role, and how you contributed to the team's success.

Example

“I collaborated with a multidisciplinary team on a project aimed at improving recommendation systems. My role involved developing the machine learning algorithms, while others focused on data collection and user experience. Our combined efforts led to a significant increase in user engagement metrics.”

4. What ethical considerations do you take into account in your research?

This question is important given the focus on responsible AI at Rakuten.

How to Answer

Discuss the ethical implications of AI and how you ensure your research adheres to ethical standards.

Example

“I prioritize ethical considerations by ensuring that my models are transparent and fair. I conduct bias assessments on my datasets and implement strategies to mitigate any identified biases, ensuring that the AI solutions I develop are responsible and equitable.”

5. How would you approach a long-term research project?

This question assesses your project management skills.

How to Answer

Outline your strategy for planning, executing, and evaluating a long-term research project.

Example

“I would start by defining clear research objectives and milestones. I would then develop a detailed project plan, including timelines and resource allocation. Regular check-ins with stakeholders would ensure alignment and allow for adjustments based on feedback and findings.”