Verily Life Sciences Data Scientist Interview Questions + Guide in 2025

Verily Life Sciences, a subsidiary of Alphabet, leverages data-driven approaches to transform health management and healthcare delivery through innovative solutions.

As a Data Scientist at Verily, you will play a critical role in designing and developing advanced conversational systems powered by Large Language Model (LLM) agents, particularly for healthcare applications. Your responsibilities will include collaborating with cross-functional teams to translate business requirements into technical specifications, conducting research on the latest advancements in AI and ML technologies, and prototyping algorithms to optimize system performance. The ideal candidate will possess a strong foundation in data science, with competencies in Python, LLM technologies, and a background in healthcare-related AI applications. Additionally, an agile mindset and a passion for creating impactful solutions are essential for success in this role, aligning with Verily's commitment to precision health.

This guide is designed to help you prepare for your interview by providing insights into the expectations and requirements of the Data Scientist role at Verily. Understanding these elements will give you a competitive edge as you showcase your skills and experiences.

The interview process for a Data Scientist role at Verily Life Sciences is structured and thorough, reflecting the company's commitment to finding the right candidates who can contribute to their mission of transforming healthcare through data-driven solutions. The process typically includes several stages, each designed to assess different aspects of a candidate's skills and fit for the role.

1. Initial Screening

The process begins with an initial phone screening, usually lasting around 30 minutes. This call is typically conducted by a recruiter or a member of the talent acquisition team. During this conversation, candidates discuss their background, interest in the role, and relevant experiences. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist position, ensuring candidates understand what is expected of them.

2. Technical Screening

Following the initial screening, candidates will undergo a technical screening, which is often conducted via video call. This session typically lasts about 45 minutes and focuses on assessing the candidate's technical skills, particularly in areas such as statistics, algorithms, and Python programming. Candidates may be asked to solve coding problems or discuss their previous work related to AI/ML applications, especially in healthcare contexts.

3. Onsite Interviews

The next stage involves a series of onsite interviews, which may be conducted virtually. This phase usually consists of four to five back-to-back interviews, each lasting approximately 45 minutes. Interviewers may include data scientists, engineers, and other cross-functional team members. The focus during these interviews is on problem-solving abilities, technical expertise, and behavioral questions that assess how candidates work in teams and handle challenges. Candidates should be prepared for coding challenges that may involve LeetCode-style questions, particularly around algorithms and data structures.

4. Final Interview with Hiring Manager

The final step in the interview process is typically a conversation with the hiring manager. This interview serves to evaluate the candidate's fit within the team and the organization as a whole. It may include discussions about the candidate's long-term career goals, their approach to collaboration, and how they can contribute to Verily's mission.

Throughout the process, candidates are encouraged to ask questions about the role, team dynamics, and the company's projects, as this demonstrates their genuine interest in the position and helps them assess if Verily is the right fit for them.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

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

Understand the Interview Structure

The interview process at Verily typically consists of multiple stages, including an initial phone screen, a technical screening, and several rounds of technical interviews. Familiarize yourself with this structure and prepare accordingly. Expect to encounter a mix of behavioral and technical questions, with a strong emphasis on coding and problem-solving skills. Knowing the format will help you manage your time and energy throughout the process.

Highlight Relevant Experience

When discussing your background, focus on your experience with AI/ML applications, particularly in healthcare. Be prepared to share specific examples of projects where you utilized LLM technologies, Python, and machine learning algorithms. Tailor your responses to demonstrate how your skills align with Verily's mission of using data to improve health outcomes.

Brush Up on Technical Skills

Given the emphasis on algorithms, statistics, and Python, ensure you are well-versed in these areas. Practice coding problems on platforms like LeetCode, focusing on medium to hard-level questions. Be ready to discuss your thought process and the time and space complexity of your solutions. Additionally, familiarize yourself with the latest advancements in generative AI and LLM technologies, as these are crucial for the role.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you work within a team. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Highlight instances where you successfully collaborated with cross-functional teams or navigated challenges in a project. This will showcase your ability to thrive in Verily's collaborative environment.

Communicate Clearly and Confidently

Throughout the interview, communicate your thoughts clearly and confidently. When solving coding problems, articulate your reasoning and approach as you work through the solution. This not only demonstrates your technical skills but also your ability to collaborate and engage with others, which is essential in a team-oriented culture like Verily's.

Stay Informed About the Company

Research Verily's recent projects, initiatives, and values. Understanding the company's focus on precision health and data-driven solutions will allow you to tailor your responses and show genuine interest in their mission. This knowledge can also help you formulate insightful questions to ask your interviewers, demonstrating your enthusiasm for the role.

Be Prepared for a Fast-Paced Environment

Interviews at Verily can be intense and fast-paced, similar to those at Google. Be ready to think on your feet and adapt to the flow of the conversation. If you encounter a challenging question, take a moment to gather your thoughts before responding. This will help you maintain composure and present your best self.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. This is not only courteous but also reinforces your interest in the position. If you don't hear back within a reasonable timeframe, consider following up to inquire about your application status, as communication can sometimes be delayed.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Data Scientist role at Verily Life Sciences. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Verily Life Sciences. The interview process will likely focus on your technical skills, particularly in machine learning, statistics, and programming, as well as your ability to work collaboratively in a healthcare context. Be prepared to demonstrate your knowledge of AI/ML applications, particularly in healthcare, and your problem-solving abilities.

Machine Learning

1. Can you explain the difference 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 supervised and unsupervised learning, providing examples of each. Highlight the types of problems each method is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting patient outcomes based on historical data. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, like clustering patients based on similar health metrics.”

2. How would you approach building a machine learning model for a healthcare application?

This question assesses your practical application of machine learning in a relevant context.

How to Answer

Outline the steps you would take, from understanding the problem and gathering data to model selection and evaluation.

Example

“I would start by collaborating with healthcare professionals to define the problem clearly. Next, I would gather relevant data, ensuring it is clean and representative. After selecting an appropriate model, I would train it and evaluate its performance using metrics like accuracy and F1 score, iterating as necessary based on feedback.”

3. What techniques would you use to handle imbalanced datasets in healthcare?

Imbalanced datasets are common in healthcare, and knowing how to address them is essential.

How to Answer

Discuss various techniques such as resampling methods, using different evaluation metrics, or employing algorithms that are robust to class imbalance.

Example

“To handle imbalanced datasets, I would consider techniques like oversampling the minority class or undersampling the majority class. Additionally, I would use evaluation metrics like precision, recall, and the F1 score instead of accuracy to better assess model performance.”

4. Can you describe a project where you implemented a machine learning model in a healthcare setting?

This question allows you to showcase your practical experience.

How to Answer

Provide a brief overview of the project, your role, the challenges faced, and the outcomes.

Example

“In my previous role, I developed a predictive model to identify patients at risk of readmission. I collaborated with clinicians to gather data from EHRs, implemented a random forest model, and achieved a 20% reduction in readmissions, which was a significant improvement for the hospital.”

Statistics & Probability

1. How do you assess the statistical significance of your results?

Statistical significance is crucial in validating findings in healthcare.

How to Answer

Explain the concept of p-values and confidence intervals, and how you would apply them in your analysis.

Example

“I assess statistical significance by calculating p-values and confidence intervals. A p-value less than 0.05 typically indicates that the results are statistically significant, meaning there is a less than 5% chance that the observed results are due to random variation.”

2. What is the Central Limit Theorem, and why is it important?

Understanding fundamental statistical concepts is key for data analysis.

How to Answer

Define the Central Limit Theorem and discuss its implications for statistical inference.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is important because it allows us to make inferences about population parameters even when the population distribution is unknown.”

3. How would you handle missing data in a dataset?

Handling missing data is a common challenge in data science.

How to Answer

Discuss various strategies such as imputation, deletion, or using algorithms that can handle missing values.

Example

“I would first analyze the pattern of missing data to determine if it is random or systematic. Depending on the situation, I might use imputation techniques, such as mean or median imputation, or consider using models that can handle missing values directly.”

Programming (Python)

1. Can you describe your experience with Python for data analysis?

This question assesses your programming skills and familiarity with relevant libraries.

How to Answer

Discuss your experience with Python and specific libraries like Pandas, NumPy, and Scikit-learn.

Example

“I have extensive experience using Python for data analysis, particularly with libraries like Pandas for data manipulation, NumPy for numerical computations, and Scikit-learn for building machine learning models. I often use these tools to clean data, perform exploratory data analysis, and implement predictive models.”

2. How would you optimize a slow-running Python script?

Performance optimization is crucial in data processing tasks.

How to Answer

Discuss techniques such as profiling the code, using efficient data structures, or leveraging libraries like NumPy for performance improvements.

Example

“I would start by profiling the script to identify bottlenecks. Then, I might optimize the code by using vectorized operations with NumPy instead of loops, or by utilizing more efficient data structures like dictionaries for lookups.”

3. Can you explain how you would implement a machine learning model in Python?

This question tests your practical coding skills.

How to Answer

Outline the steps you would take to implement a model, including data preparation, model training, and evaluation.

Example

“I would begin by importing the necessary libraries, such as Pandas for data manipulation and Scikit-learn for modeling. After loading and preprocessing the data, I would split it into training and testing sets, train the model using the training data, and evaluate its performance on the test set using appropriate metrics.”

4. What is your experience with version control systems like Git?

Version control is essential for collaborative projects.

How to Answer

Discuss your familiarity with Git and how you use it in your projects.

Example

“I regularly use Git for version control in my projects. I create branches for new features, commit changes with clear messages, and use pull requests for code reviews. This practice helps maintain a clean project history and facilitates collaboration with team members.”