Verily Data Scientist interview questions & guide 2026

As a Data Scientist at Verily, you play a critical role at the intersection of advanced technology and precision health. Launched from Google X, Verily operates with a clear mandate: to generate and activate data from clinical, social, behavioral, and real-world sources to transform how people manage health and how healthcare is delivered. In this position, you tackle complex, multi-source integrations, build longitudinal datasets from electronic health records and claims, and deploy machine learning models and large language models that unlock value from structured and unstructured healthcare data.

Your work directly influences products and platforms that support research, evidence generation, and care decisions across the healthcare ecosystem. You collaborate closely with cross-functional partners—including engineers, product managers, and clinical subject matter experts—to build scalable tools, analyze intricate real-world data, and navigate the messy, nuanced reality of medical information. Success in this role requires a balance of rigorous technical execution, deep data intuition, and the agility to thrive in an environment characterized by scientific ambiguity and high impact.

The questions you will encounter are drawn directly from real reported interview experiences across hiring loops for the Data Scientist role. While exact questions vary by team and product focus, these examples illustrate the core patterns and levels of complexity you should expect.

Product-Sense and Metric Design

These questions test your ability to translate ambiguous healthcare or product goals into measurable metrics, diagnose unexpected drops in key performance indicators, and evaluate product success.

• How would you design product metrics for a new longitudinal health registry platform?
• Your daily active user engagement metric dropped by fifteen percent week-over-week. How would you investigate and diagnose the root cause?

Preparing for the Data Scientist interview loop at Verily requires a structured approach that bridges core data science fundamentals with domain-specific awareness. Interviewers look for candidates who can seamlessly transition from writing clean production-grade code to discussing clinical data complexities and experimental design.

Role-related knowledge – This evaluation area covers your technical proficiency in Python, machine learning, natural language processing, and statistical modeling. Interviewers expect you to demonstrate fluency in both classical statistical methods and modern deep learning or large language model techniques, particularly as applied to real-world healthcare data.

Problem-solving ability – You will be assessed on how you approach messy, open-ended analytical problems with high levels of ambiguity. Strong candidates methodically clarify requirements, formulate hypotheses, identify edge cases, and reason through the limitations of available data sources.

Leadership and collaboration – Because you will work in diverse cross-functional teams alongside engineers, product managers, and clinical experts, your communication skills are paramount. You must be able to articulate complex technical trade-offs clearly to both technical and non-technical audiences.

The interview journey for the Data Scientist role typically begins with a recruiter screening call focused on your background, interest in health technology, and high-level qualifications. If you pass this initial filter, you will speak with the hiring manager for a deeper dive into your past projects, technical expertise, and domain experience. Candidates who advance past the hiring manager screen are invited to a virtual onsite or super day consisting of multiple focused interviews. These sessions span coding assessments in Python, technical deep dives into machine learning and large language models, system design or data architecture discussions, and a dedicated behavioral or team fit round. The pace can move quickly when the team identifies a strong match, but expect rigorous technical probing across multiple sessions.

This visual timeline illustrates the multi-stage evaluation structure, moving from initial recruiter and hiring manager screens into intensive technical rounds and final team alignment. Plan your preparation to peak during the multi-round technical and coding phases, ensuring you maintain stamina and focus across all interview domains. Keep in mind that specific teams—such as those focusing on real-world data registries versus AI agents—may place slightly different emphasis on LLM applications versus core data pipeline architecture.

Interviewers at Verily evaluate candidates across several specific competency pillars. Understanding what each area targets will help you direct your preparation effectively.

Statistical Rigor and Experimentation

This area evaluates your command of probability, statistical inference, and experimental design. You must be comfortable reasoning about population biases, confounding variables, and hypothesis testing in settings where data is often observational rather than purely experimental.

Be ready to go over:

• A/B testing frameworks and sample size calculations
• Identifying and mitigating experimentation pitfalls such as novelty effects or sample ratio mismatches
• Methods for establishing statistical significance under multiple testing constraints
• Advanced concepts (less common): Causal inference methods like propensity score matching and instrumental variables for observational health data.

Example questions or scenarios:

• "How would you measure the impact of a clinical product intervention when randomized controlled trials are ethically or practically impossible?"
• "Walk me through how you detect and correct for selection bias in a longitudinal patient registry dataset."

Data Manipulation and SQL Mastery

Data engineering and querying fundamentals are essential for extracting and transforming messy real-world datasets like electronic health records and claims data.

Be ready to go over:

• Complex aggregations and filtering using SQL window functions
• Multi-source data integration, deduplication, and reconciliation logic
• Creating derived clinical features from raw event logs
• Advanced concepts (less common): Query performance tuning for massive distributed databases and handling unstructured JSON blobs within relational schemas.

Example questions or scenarios:

• "Write a query to track patient journey milestones across disparate data tables with overlapping date ranges."
• "How do you validate data integrity after performing a complex multi-table join on millions of medical records?"

Machine Learning and Applied AI

Given the focus on AI capabilities and unstructured medical text, interviewers will test your ability to build, evaluate, and deploy models on sparse or noisy data.

Be ready to go over:

• Supervised and unsupervised learning techniques applied to healthcare data
• Large language model prompt optimization, fine-tuning, and retrieval augmented generation
• Evaluation metrics for imbalanced datasets and clinical concept abstraction
• Advanced concepts (less common): Model drift detection in production healthcare environments and transformer architecture trade-offs.

Example questions or scenarios:

• "How would you design a pipeline to extract specific clinical concepts from unstructured physician notes?"
• "What strategies do you use to prevent overfitting when training a model on sparsely labeled medical datasets?"

As a Data Scientist at Verily, your day-to-day work centers on transforming complex healthcare data into actionable insights and scalable AI products. You work closely with cross-functional partners to design, build, and maintain longitudinal datasets that integrate electronic health records, claims data, and prospective collections. A significant portion of your time is spent developing and deploying machine learning models and large language model tools capable of extracting structured concepts from unstructured clinical text.

You also act as an internal expert on data capabilities and limitations, conducting rigorous data quality assessments and solving difficult, non-routine analysis problems. Beyond technical execution, you communicate your findings and methodologies through clear reports and presentations tailored to diverse audiences, bridging the gap between rigorous quantitative research and real-world health applications.

To be competitive for the Data Scientist position, you must combine solid technical credentials with practical experience handling complex data domains.

• Must-have skills – Advanced degree in a quantitative discipline such as statistics, computer science, biomedical informatics, or applied mathematics; strong proficiency in Python; 3+ years of experience applying advanced machine learning and AI techniques to clinical or complex real-world data; direct experience curating electronic health records or similar structured and unstructured datasets.
• Nice-to-have skills – Ph.D. in Computer Science or a related engineering field; familiarity with medical terminologies and ontologies; experience developing production software with solid software engineering practices; direct work with transformer-based deep learning models, fine-tuning, and retrieval augmented generation.
• Soft skills – Exceptional cross-functional communication, tolerance for ambiguity, creative and methodical problem-solving, and the ability to collaborate effectively with clinical subject matter experts.

Q: How difficult is the interview loop at Verily? The interview loop is moderately to highly rigorous, reflecting the company's Alphabet heritage and the technical complexity of precision health data. Expect challenging technical rounds in Python, coding, and machine learning, alongside deep discussions of your past project experience.

Q: How much preparation time should I plan for? Most candidates benefit from four to six weeks of dedicated preparation. Focus your time on refreshing advanced SQL, practicing coding problems, reviewing A/B testing and experimentation pitfalls, and brushing up on recent advancements in LLMs and real-world data curation.

Q: What is the company culture like during the interview process? Feedback across candidate experiences indicates that interviewers are generally friendly, professional, and encouraging during the interactive sessions. However, administrative communication can sometimes feel abrupt, so maintaining clear channels with your recruiter is recommended.

Q: Is remote work an option for this role? Certain positions offer flexibility or remote arrangements depending on the specific team, though many roles are anchored near core hubs such as Mountain View, San Bruno, or Boston. Check individual job descriptions or confirm with your recruiter during the initial screen.

Q: What is the typical timeline from initial screen to offer? The process typically spans several weeks, moving from recruiter and hiring manager screens to a multi-round virtual onsite and concluding with team fit discussions and offer stages.

• Anchor answers in healthcare context: When discussing machine learning or metrics, always acknowledge the unique constraints of health data, such as missingness, privacy, and clinical relevance.
• Clarify ambiguities proactively: Real-world data problems are rarely well-defined. When given an open-ended scenario, verbalize your assumptions and ask clarifying questions before diving into a solution.
• Demonstrate cross-functional empathy: Highlight experiences where you successfully translated technical concepts for clinical or product stakeholders without relying on jargon.
• Prepare structured examples for behavioral rounds: Use the STAR method to frame your experiences, paying particular attention to how you handled project roadblocks or data quality crises.
• Brush up on your coding fundamentals: Do not neglect live coding preparation in Python, as unexpected coding sections do appear in the technical loops.

Stepping into the Data Scientist role at Verily offers a unique opportunity to apply data science and artificial intelligence to meaningful challenges in precision health and healthcare delivery. By mastering core technical areas—ranging from advanced SQL window functions and experimentation design to large language model applications and real-world data curation—you position yourself to excel across the evaluation rubric. Approach your preparation with deliberate practice, structured problem-solving, and a clear understanding of the healthcare domain's unique complexities.

The compensation data reflects base salary ranges for full-time positions at Verily, typically falling between $110,000 and $177,000 USD depending on level, location, and role specifics, and excludes additional components such as bonuses, equity grants, and comprehensive benefits. Candidates can explore additional interview insights, practice questions, and preparation resources on Dataford to refine their readiness further. With focused preparation and a rigorous approach to both technical and product-sense dimensions, you are well-equipped to navigate the interview loop and make a compelling case for your impact at Verily.