Verily Data Scientist Interview Questions + Guide in 2025

Verily, a subsidiary of Alphabet, is dedicated to transforming healthcare through a data-driven approach, aiming to enhance how individuals manage their health and improve healthcare delivery.

As a Data Scientist at Verily, your role will involve leveraging advanced data science techniques to support the development of innovative solutions in healthcare. This position is focused on generating and analyzing real-world data (RWD) from diverse sources, including electronic health records (EHRs) and various clinical datasets, to drive evidence-based research and care decisions. Key responsibilities include creating longitudinal datasets, implementing machine learning models to analyze complex healthcare data, and effectively communicating insights to both technical and non-technical stakeholders.

To excel in this role, you should possess an advanced degree in a quantitative field, such as data science or statistics, along with significant experience in machine learning, particularly in the healthcare domain. A strong proficiency in Python, familiarity with medical terminologies, and the ability to work collaboratively in cross-functional teams are essential traits. Verily values creativity and methodical problem-solving abilities, encouraging candidates who can navigate the complexities of real-world data and contribute to the company's mission of precision health.

This guide is designed to help you prepare for your interview by providing insights into the skills and competencies that Verily seeks in a Data Scientist, enhancing your confidence and readiness for this opportunity.

The interview process for the Data Scientist role at Verily is structured to assess both technical expertise and cultural fit within the organization. Candidates can expect a multi-step process that includes various types of interviews, focusing on their ability to work with real-world data and collaborate with cross-functional teams.

1. Initial Screening

The first step in the interview process is an initial screening, typically conducted by a recruiter. This 30- to 45-minute phone call serves to gauge your interest in the role and the company, as well as to discuss your background and experience. The recruiter will ask about your technical skills, particularly in data science and machine learning, and assess your alignment with Verily's mission and values.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via video call. This assessment focuses on your proficiency in data science methodologies, including machine learning algorithms, data manipulation, and statistical analysis. You may be asked to solve problems in real-time, demonstrating your coding skills, particularly in Python, and your ability to work with real-world datasets, such as electronic health records (EHRs).

3. Behavioral Interviews

Candidates will then participate in one or more behavioral interviews. These interviews are designed to evaluate how you approach problem-solving, teamwork, and communication. Expect questions that explore your past experiences, particularly those that highlight your ability to work cross-functionally and handle ambiguity. The interviewers will be looking for evidence of your creative and methodical problem-solving skills, as well as your ability to communicate complex technical concepts to both technical and non-technical audiences.

4. Onsite or Final Interview

The final stage of the interview process may involve an onsite interview or a series of final video interviews. This stage typically includes multiple rounds with different team members, including data scientists, engineers, and possibly clinical experts. Each round will delve deeper into your technical skills, your understanding of healthcare data, and your ability to contribute to Verily's projects. You may also be asked to present a case study or a project you have worked on, showcasing your analytical skills and your approach to data-driven decision-making.

5. Cultural Fit Assessment

Throughout the interview process, Verily places a strong emphasis on cultural fit. Interviewers will assess how well you align with the company's values, such as innovation, collaboration, and respect for individuals. Be prepared to discuss how you embody these values in your work and how you can contribute to fostering an inclusive and equitable workplace.

As you prepare for your interviews, consider the specific questions that may arise regarding your technical expertise and experiences in the field.

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

Understand the Mission and Values

Verily is deeply committed to transforming healthcare through data-driven solutions. Familiarize yourself with their mission of precision health and how they leverage diverse data sources to improve health outcomes. Reflect on how your personal values align with Verily's emphasis on innovation, collaboration, and respect for individuals. Be prepared to discuss how your background and experiences can contribute to their mission.

Showcase Your Technical Expertise

As a Data Scientist, you will be expected to demonstrate a strong command of machine learning, AI techniques, and data curation, particularly in the context of real-world data. Brush up on your knowledge of Python, LLMs, and NLP tools, and be ready to discuss specific projects where you applied these skills. Highlight your experience with EHRs and the complexities of clinical data, as this will be crucial in your role.

Prepare for Cross-Functional Collaboration

Verily values teamwork and cross-functional collaboration. Be ready to share examples of how you have successfully worked with diverse teams in the past. Discuss your approach to problem-solving in ambiguous situations and how you communicate complex technical concepts to non-technical stakeholders. This will demonstrate your ability to thrive in Verily's collaborative environment.

Emphasize Your Problem-Solving Skills

The role requires creative and methodical problem-solving abilities. Prepare to discuss specific challenges you've faced in your previous work and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on how you identified needs, formed hypotheses, and generated robust results.

Communicate Clearly and Effectively

Verily places a strong emphasis on clear communication. Practice articulating your technical methods and results in a way that is accessible to both technical and non-technical audiences. Consider preparing a brief presentation or summary of a past project that showcases your ability to communicate complex ideas effectively.

Be Ready to Discuss Ethical Considerations

Given the nature of healthcare data, be prepared to discuss ethical considerations related to data privacy, security, and the responsible use of AI in healthcare. Demonstrating an understanding of these issues will show that you are not only technically proficient but also aware of the broader implications of your work.

Show Enthusiasm for Continuous Learning

Verily is at the forefront of healthcare innovation, and they value individuals who are eager to learn and adapt. Share examples of how you stay updated with the latest advancements in data science and healthcare technology. Discuss any relevant courses, certifications, or personal projects that demonstrate your commitment to continuous improvement.

Align with the Company Culture

Verily's culture emphasizes inclusion, belonging, and equitability. Be prepared to discuss how you contribute to a positive team environment and support diversity in the workplace. Reflect on your experiences and how they align with Verily's values, and be ready to share your thoughts on fostering an inclusive culture.

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

Verily Data Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Verily Data Scientist interview. The role focuses on leveraging data science to drive innovation in healthcare, particularly through the use of real-world data (RWD) and machine learning techniques. Candidates should be prepared to discuss their experience with data integration, machine learning models, and their ability to communicate complex technical concepts to diverse audiences.

Machine Learning and AI

1. Can you describe a machine learning project you worked on that involved real-world data?

This question aims to assess your practical experience with machine learning in a healthcare context.

How to Answer

Discuss the project’s objectives, the data sources you used, the models you implemented, and the outcomes. Highlight any challenges you faced and how you overcame them.

Example

“I worked on a project to predict patient readmission rates using EHR data. I integrated data from multiple sources, including clinical notes and demographic information, and used a random forest model to identify key predictors. The model improved our readmission prediction accuracy by 15%, allowing the care team to intervene earlier.”

2. How do you handle sparsely labeled datasets in your machine learning models?

This question evaluates your understanding of challenges in healthcare data.

How to Answer

Explain techniques you use to work with sparsely labeled data, such as semi-supervised learning or data augmentation.

Example

“I often employ semi-supervised learning techniques to leverage both labeled and unlabeled data. For instance, in a project analyzing patient outcomes, I used a small set of labeled data to train a model and then applied it to a larger unlabeled dataset to enhance our predictions.”

3. What experience do you have with LLMs and NLP in healthcare applications?

This question assesses your familiarity with advanced AI techniques relevant to the role.

How to Answer

Discuss specific projects where you applied LLMs or NLP, focusing on the techniques used and the impact on healthcare outcomes.

Example

“I developed an NLP pipeline to extract clinical concepts from unstructured notes in EHRs. By fine-tuning a pre-trained LLM, I was able to achieve a 90% accuracy rate in identifying relevant medical terms, which significantly improved our data curation process.”

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

This question looks for your problem-solving skills and technical expertise.

How to Answer

Outline the steps you took to identify performance issues, the methods you used to optimize the model, and the results of your efforts.

Example

“In a project predicting treatment outcomes, I noticed the model was overfitting. I implemented cross-validation, adjusted hyperparameters, and introduced regularization techniques, which improved the model’s generalization and reduced the error rate by 20%.”

5. How do you ensure the interpretability of your machine learning models, especially in a healthcare setting?

This question evaluates your understanding of the importance of model transparency in healthcare.

How to Answer

Discuss methods you use to enhance model interpretability, such as SHAP values or LIME, and why they are crucial in healthcare.

Example

“I prioritize model interpretability by using SHAP values to explain feature contributions. This is essential in healthcare, as stakeholders need to understand the rationale behind predictions to trust and act on the model’s recommendations.”

Data Integration and Management

1. Can you explain your experience with integrating multiple data sources for analysis?

This question assesses your ability to work with diverse datasets.

How to Answer

Describe specific projects where you integrated data from various sources, the challenges faced, and how you ensured data quality.

Example

“I led a project that integrated EHR data with claims data and patient-reported outcomes. I developed a robust ETL process to clean and standardize the data, which allowed us to create a comprehensive dataset for analysis, ultimately leading to more accurate insights into patient care.”

2. What strategies do you use to assess and improve data quality in your projects?

This question evaluates your approach to maintaining high data standards.

How to Answer

Discuss specific techniques you employ for data validation, cleaning, and quality assessment.

Example

“I implement a combination of automated checks and manual reviews to assess data quality. For instance, I use Python scripts to identify missing values and outliers, followed by cross-validation with clinical experts to ensure the data’s accuracy and relevance.”

3. Describe your experience with EHR data and the complexities involved.

This question focuses on your familiarity with healthcare data systems.

How to Answer

Explain your experience with EHR data, including any challenges you faced and how you addressed them.

Example

“I have worked extensively with EHR data, which often includes unstructured notes and varying formats. I developed a framework to standardize the data and used NLP techniques to extract meaningful insights, ensuring that we could effectively analyze patient outcomes.”

4. How do you approach feature engineering when working with clinical data?

This question assesses your technical skills in preparing data for modeling.

How to Answer

Discuss your methods for creating features from raw clinical data, emphasizing creativity and domain knowledge.

Example

“I focus on deriving features that capture clinical significance, such as creating a ‘comorbidity index’ from diagnosis codes. I also utilize domain knowledge to identify relevant features that may not be immediately apparent from the data alone.”

5. Can you provide an example of a time you communicated complex data findings to a non-technical audience?

This question evaluates your communication skills.

How to Answer

Describe the context, your approach to simplifying the information, and the outcome of your communication.

Example

“I presented findings from a predictive model to a group of healthcare providers. I used visualizations to illustrate key insights and avoided technical jargon, focusing instead on how the findings could impact patient care. The feedback was positive, and it led to actionable changes in their approach to patient management.”