What is a Data Scientist at Verily?

As a Data Scientist at Verily, you are positioned at the critical intersection of advanced technology, rigorous data science, and life-changing healthcare innovation. Verily, an Alphabet company, aims to bring the promise of precision health to everyone, every day. In this role, you will leverage massive, complex datasets to generate actionable insights that drive product development, optimize clinical research, and ultimately improve patient outcomes.

Your impact will be profound and multifaceted. Whether you are working as a Senior Data Scientist for Real World Data (RWD) in Mountain View or building predictive models as a Senior Data Scientist for Agents in San Bruno, your work directly influences the trajectory of healthcare products. You will tackle incredibly complex problem spaces, from analyzing electronic health records (EHR) and wearable sensor data to developing intelligent, AI-driven agents that assist in clinical decision-making and patient engagement.

This role is not for the faint of heart; it requires a unique blend of deep statistical rigor, machine learning expertise, and the ability to navigate the highly regulated, often ambiguous world of healthcare data. You can expect to work alongside cross-functional teams of software engineers, clinicians, product managers, and regulatory experts. If you are passionate about using data to solve some of the most pressing challenges in human health, this role offers unparalleled scale, complexity, and strategic influence.

Common Interview Questions

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Getting Ready for Your Interviews

Preparing for a Verily interview requires a strategic approach. You are not just proving your technical competence; you are demonstrating how you apply that competence to messy, real-world problems in a high-stakes domain.

Your interviewers will evaluate you across several core dimensions:

• Domain and Technical Expertise – You must demonstrate a deep understanding of statistical modeling, machine learning, and data manipulation. For RWD roles, this means expertise in causal inference and observational data. For Agent roles, expect a focus on modern ML, NLP, and predictive modeling.
• Problem-Solving Ability – Interviewers want to see how you structure ambiguous challenges. You will be evaluated on your ability to break down a vague healthcare or product problem, identify the right data sources, choose the appropriate methodology, and acknowledge the limitations of your approach.
• Cross-Functional Communication – Data scientists at Verily do not work in silos. You must be able to translate complex technical concepts into actionable insights for non-technical stakeholders, including medical professionals and business leaders.
• Culture Fit and "Googley-ness" – Verily shares Alphabet's cultural DNA. You will be assessed on your ability to thrive in ambiguity, your collaborative spirit, your intellectual humility, and your unwavering commitment to doing the right thing for patients and users.

Interview Process Overview

The interview process for a Data Scientist at Verily is rigorous, deeply technical, and highly structured, mirroring the high standards of its parent company, Alphabet. The process typically begins with an initial recruiter screen to align on your background, role preferences (e.g., RWD vs. Agents), and location expectations. If there is a mutual fit, you will move on to a technical phone screen. This screen usually involves a mix of coding (often in Python or SQL) and foundational statistics or machine learning questions, conducted via a shared coding environment.

If you pass the technical screen, you will be invited to the virtual onsite loop. The onsite stage is comprehensive, usually consisting of four to five distinct rounds. You will face a dedicated coding and data manipulation round, a deep-dive technical round focused on statistics and machine learning, a product sense or case study round tailored to healthcare scenarios, and a behavioral round. Verily places a heavy emphasis on how you handle the nuances of healthcare data, so expect your technical rounds to be heavily contextualized around real-world clinical or product problems.

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This visual timeline outlines the typical progression from the initial recruiter screen through the technical assessments and the final onsite loop. You should use this timeline to pace your preparation, ensuring you are ready for the coding screen early on while reserving time to practice complex, open-ended healthcare case studies for the onsite rounds. Note that the exact sequence and focus of the onsite rounds may vary slightly depending on whether you are interviewing for the RWD or Agents team.

Deep Dive into Evaluation Areas

To succeed, you must excel across several distinct technical and strategic domains. Your interviewers will probe deeply into your past experiences and present you with novel, ambiguous scenarios.

Real World Data (RWD) & Statistical Inference

For roles focused on RWD, this is the most critical evaluation area. Interviewers want to know if you can draw valid conclusions from messy, observational data that was not collected for research purposes (like insurance claims or EHRs). Strong performance means demonstrating a rigorous understanding of bias, confounding variables, and causal inference.

Be ready to go over:

• Causal Inference – Propensity score matching, instrumental variables, and difference-in-differences.
• Study Design – Retrospective vs. prospective cohort studies, survival analysis (Kaplan-Meier, Cox proportional hazards).
• Data Quality & Bias – Handling missing data, selection bias, and measurement error in healthcare datasets.
• Advanced concepts (less common) – Targeted maximum likelihood estimation (TMLE), advanced imputation techniques.

Example questions or scenarios:

• "How would you design an observational study to determine if a new wearable device reduces hospital readmission rates?"
• "Explain how you would handle missing lab results in an EHR dataset when building a predictive model."
• "What are the key differences between randomized controlled trials (RCTs) and RWD studies, and how do you mitigate the limitations of RWD?"

Machine Learning & AI Agents

If you are interviewing for the Agents team or a highly predictive role, your ML fundamentals must be rock solid. Interviewers will evaluate your ability to select, train, evaluate, and deploy models that power intelligent systems. A strong candidate will balance model complexity with interpretability, which is vital in healthcare.

Be ready to go over:

• Predictive Modeling – Classification, regression, tree-based models (XGBoost, Random Forest), and deep learning fundamentals.
• NLP & Large Language Models (LLMs) – Text classification, entity extraction from clinical notes, and building conversational agents.
• Model Evaluation – Choosing the right metrics (Precision, Recall, F1, AUC-ROC) and understanding the clinical cost of false positives vs. false negatives.
• Advanced concepts (less common) – Reinforcement learning for personalized interventions, federated learning for privacy-preserving ML.

Example questions or scenarios:

• "How would you build an AI agent to triage patient messages based on urgency?"
• "Walk me through how you would evaluate a machine learning model designed to predict sepsis in an ICU setting."
• "What are the trade-offs between using a complex deep learning model versus a simpler, interpretable logistic regression model for clinical decision support?"