Roivant Sciences Machine Learning Engineer Interview Questions + Guide in 2025

Roivant Sciences is a biopharmaceutical company focused on delivering innovative medicines to patients faster through technology and a commitment to agility.

As a Machine Learning Engineer at Roivant Sciences, you will play a pivotal role in developing and implementing machine learning models and algorithms that drive insights from complex biomedical data. Your key responsibilities will include collaborating with cross-functional teams to identify use cases for machine learning, designing and executing experiments to validate models, and integrating these models into production systems.

The ideal candidate will possess strong programming skills, particularly in Python, and have a solid understanding of machine learning frameworks and libraries. Experience with data processing and analysis, as well as familiarity with cloud computing platforms, will be essential in this role. Additionally, a passion for solving real-world healthcare problems and the ability to communicate technical concepts to both technical and non-technical stakeholders will set you apart as an exceptional fit for Roivant. This aligns with the company's emphasis on teamwork and customer-centric approaches in their innovative solutions.

This guide aims to equip you with the insights and knowledge necessary to excel in your interview process by highlighting the specific skills and experiences that Roivant values most in a Machine Learning Engineer.

The interview process for a Machine Learning Engineer at Roivant Sciences is designed to assess both technical skills and cultural fit within the organization. The process typically unfolds in several distinct stages:

1. Initial Screening

The first step usually involves a phone screen with a recruiter. This conversation is an opportunity for the recruiter to gauge your interest in Roivant Sciences, discuss your background, and understand your motivations for applying. Expect questions that explore your experience and how it aligns with the company's mission and values.

2. Digital Interview

Following the initial screening, candidates often participate in a digital interview using an online platform like HireVue. This stage consists of a series of pre-recorded questions that you will need to answer within a limited timeframe. The questions may cover both behavioral aspects and your technical experience, such as projects you've worked on or challenges you've faced. Be prepared to articulate your thoughts clearly and concisely, as the time constraints can be challenging.

3. Technical Assessment

Candidates who progress past the digital interview may be required to complete a coding challenge. This assessment typically focuses on your programming skills and problem-solving abilities, often involving tasks related to machine learning algorithms or data manipulation. The coding challenge is designed to evaluate your technical proficiency and your approach to solving real-world problems.

4. Behavioral and Technical Interviews

Successful candidates will then move on to a series of interviews, which may be conducted virtually or in-person. These interviews often include both behavioral and technical components. You may be asked to collaborate with a team on a problem-solving exercise, demonstrating your ability to work effectively with others. Expect questions that delve into your past experiences, teamwork, and how you handle challenges in a professional setting.

5. Final Presentation

In some cases, the interview process culminates in a final presentation where you will showcase a work product or project relevant to the role. This presentation allows you to demonstrate your technical knowledge, decision-making process, and ability to communicate complex ideas to a technical audience. Be prepared to answer questions from the interview panel regarding your approach and the rationale behind your decisions.

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

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

Understand the Interview Format

Roivant Sciences utilizes a mix of digital and in-person interviews, including pre-recorded video responses and coding challenges. Familiarize yourself with the HireVue platform, as many candidates have reported using it for initial screenings. Practice answering behavioral questions concisely, as you will often have limited time to respond. This will help you feel more comfortable and prepared when faced with the time constraints during the actual interview.

Prepare for Behavioral Questions

Behavioral questions are a significant part of the interview process at Roivant. Reflect on your past experiences and be ready to discuss specific situations where you demonstrated teamwork, problem-solving, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process clearly and effectively within the time limits.

Showcase Your Technical Projects

During the interviews, you may be asked to describe technical projects you have worked on. Be prepared to discuss the challenges you faced, the solutions you implemented, and the impact of your work. Highlight your experience with machine learning algorithms, data processing, and any relevant technologies. This will not only demonstrate your technical expertise but also your ability to communicate complex ideas effectively.

Emphasize Client Interaction Skills

Roivant values candidates who can work well with clients and stakeholders. Be ready to discuss your experience in client-facing roles or projects where you collaborated with non-technical teams. Highlight your communication skills and your ability to translate technical concepts into layman's terms, as this will resonate well with the company’s focus on client relationships.

Engage with Company Culture

Roivant Sciences has a unique culture that emphasizes innovation and collaboration. Research the company’s values and recent initiatives to understand what they prioritize. During the interview, express your enthusiasm for their mission and how your personal values align with theirs. This will help you stand out as a candidate who is not only technically proficient but also a cultural fit.

Practice Coding Challenges

While many candidates found the coding challenges to be straightforward, it’s essential to practice coding problems relevant to machine learning and data manipulation. Brush up on your Python skills and familiarize yourself with common algorithms and data structures. Consider using platforms like LeetCode or HackerRank to simulate the coding interview experience.

Be Yourself

Lastly, while it’s important to prepare, don’t forget to be authentic. Roivant is looking for candidates who are not only skilled but also passionate about their work. Share your genuine interests in machine learning and how you envision contributing to the company’s goals. This authenticity can leave a lasting impression on your interviewers.

By following these tips, you will be well-prepared to navigate the interview process at Roivant Sciences and showcase your qualifications effectively. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Roivant Sciences. The interview process will likely assess both your technical skills and your ability to work collaboratively within a team. Be prepared to discuss your experiences, problem-solving abilities, and your passion for the role and the company.

Technical Skills

1. Describe a machine learning project you have worked on and the impact it had.

Roivant is interested in understanding your practical experience with machine learning and how your work has contributed to real-world applications.

How to Answer

Focus on the specifics of the project, including the problem you were solving, the techniques you used, and the results achieved. Highlight any metrics that demonstrate the impact of your work.

Example

“I worked on a predictive model for patient outcomes in clinical trials, utilizing random forests and logistic regression. This model improved our ability to identify high-risk patients by 30%, which allowed the clinical team to tailor interventions more effectively.”

2. What coding languages and frameworks are you most comfortable with, and why?

This question assesses your technical proficiency and familiarity with tools relevant to the role.

How to Answer

Mention the languages and frameworks you have experience with, and explain why you prefer them based on your past projects or their applicability to machine learning tasks.

Example

“I am most comfortable with Python and TensorFlow due to their extensive libraries and community support for machine learning. I have used them in various projects, including deep learning applications for image recognition.”

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

Understanding fundamental concepts in machine learning is crucial for this role.

How to Answer

Provide a clear and concise definition of both terms, along with examples of when each would be used.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior without predefined categories.”

4. Describe a time when you had to optimize a machine learning model. What steps did you take?

This question evaluates your problem-solving skills and your approach to improving model performance.

How to Answer

Discuss the specific model you optimized, the challenges you faced, and the techniques you employed to enhance its performance.

Example

“I optimized a recommendation system by implementing hyperparameter tuning and feature selection. By using grid search and cross-validation, I improved the model’s accuracy by 15%, which significantly enhanced user engagement.”

5. How do you handle missing data in a dataset?

This question tests your understanding of data preprocessing techniques.

How to Answer

Explain the methods you use to address missing data, including imputation techniques or the decision to remove certain data points.

Example

“I typically handle missing data by first analyzing the extent and pattern of the missingness. If it’s minimal, I might use mean imputation. For larger gaps, I consider using predictive modeling to estimate missing values or, if appropriate, removing those records entirely.”

Behavioral Questions

1. Why are you interested in working at Roivant Sciences?

This question gauges your motivation and alignment with the company’s mission.

How to Answer

Discuss your passion for the healthcare industry and how Roivant’s innovative approach resonates with your career goals.

Example

“I am drawn to Roivant Sciences because of its commitment to transforming healthcare through technology. I admire the company’s focus on accelerating drug development and believe my skills in machine learning can contribute to impactful solutions in this space.”

2. Describe a situation where you and your team thrived. What contributed to that success?

This question assesses your teamwork and collaboration skills.

How to Answer

Share a specific example that highlights your role in the team’s success and the factors that led to a positive outcome.

Example

“In a recent project, our team thrived by fostering open communication and leveraging each member’s strengths. I took the initiative to organize regular check-ins, which helped us stay aligned and ultimately led to the successful launch of our product ahead of schedule.”

3. Tell us about a time you faced a difficult situation and how you handled it.

This question evaluates your problem-solving abilities and resilience.

How to Answer

Describe the challenge, your approach to resolving it, and the outcome, emphasizing what you learned from the experience.

Example

“I faced a significant challenge when a critical model I developed was underperforming. I took the initiative to conduct a thorough analysis, identified issues with feature selection, and collaborated with my team to refine our approach. This led to a successful model revision and improved results.”

4. What are you passionate about in the field of machine learning?

This question allows you to express your enthusiasm and commitment to the field.

How to Answer

Share specific areas of machine learning that excite you and how they relate to your career aspirations.

Example

“I am particularly passionate about the intersection of machine learning and healthcare. The potential to leverage data to improve patient outcomes and streamline processes is incredibly motivating for me, and I am eager to contribute to advancements in this area.”

5. Describe a time when you impressed your teammates. What did you do?

This question assesses your ability to contribute positively to team dynamics.

How to Answer

Provide an example that showcases your skills or contributions that were particularly valuable to your team.

Example

“I impressed my teammates during a hackathon by developing a prototype for a predictive analytics tool in just a few hours. I utilized my knowledge of machine learning algorithms to create a working model, which not only showcased my technical skills but also inspired the team to think creatively about our project.”