Okay, I've searched for full-time Data Scientist roles specializing in Generative AI and Large Language Models in San Bruno, CA, requiring 3+ years of experience in the healthcare or tech industry. I have found 72 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Scientist role at Verily positions the candidate at the intersection of advanced AI, healthcare data, and applied research within a complex, real-world evidence (RWE) environment. Fundamentally, this person is tasked with leveraging large language models (LLMs) and generative AI to enhance the extraction, curation, and interpretation of longitudinal healthcare datasets derived from diverse sources such as electronic health records (EHRs) and claims data. The job emphasizes constructing scalable, reusable AI solutions that bridge structured and unstructured clinical information to support precise health outcomes. The qualifications underscore the need for deep technical expertise in machine learning, particularly with transformer architectures, LLM prompt engineering, and fine-tuning methods, reflecting the criticality of building efficient models that perform well in specialized digital health contexts. Collaborating across cross-functional teams suggests this role requires not only strong technical skills but also clear communication skills to translate complex AI methodologies for broader stakeholder comprehension. The implicit challenges likely include navigating heterogeneous and noisy data, integrating multi-modal healthcare information, and aligning AI applications with clinical relevance and regulatory constraints. Success in this role means delivering robust, production-ready models and AI agents that materially advance Verily’s mission to revolutionize precision health through data-driven insights.

Company Analysis:

Verily, as an Alphabet subsidiary, stands as a progressive leader at the frontier of precision health, innovating through deep data integration and AI-driven approaches to transform healthcare delivery. It occupies a niche that blends technology and healthcare research, leveraging vast, multifaceted datasets and cutting-edge AI tools to move beyond generalized treatments toward personalized medicine. The company culture likely values scientific rigor, innovation, and cross-disciplinary collaboration, with a strong orientation toward impact and ethical application of technology in healthcare. This role aligns within a broader ecosystem of interdisciplinary teams, demanding agility and adaptability amidst evolving research and regulatory environments. As Verily is a larger organization with Alphabet's backing, there is both stability and a high expectation for thought leadership and technical excellence. The role’s strategic significance lies in enabling scalable AI solutions that can unlock complex data insights, directly fueling Verily’s growth in evidence generation and precision health. Candidates should be prepared to thrive in a fast-evolving, mission-driven setting that not only demands technical mastery but also a proactive stance on cross-team influence and continuous learning.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to collaborate with a diverse, cross-functional team to develop a data science solution. What challenges did you face and how did you address them?
• Behavioral: Describe an experience where you encountered ambiguous or messy real-world data. How did you handle data quality and ensure your model’s reliability?
• Behavioral: Give an example of when you had to explain a complex machine learning concept to a non-technical stakeholder. How did you ensure their understanding?
• Behavioral: Share a situation where you had to balance research innovation with practical deployment constraints. How did you prioritize competing demands?
• Technical: How would you design and deploy a large language model based agent to extract clinical concepts from unstructured EHR notes? What key steps and techniques would you use?
• Technical: Can you walk me through your approach to prompt optimization and fine-tuning of transformer-based models in a healthcare context?
• Technical: What strategies do you apply for integrating and reconciling multi-source healthcare data to build a comprehensive dataset suitable for AI modeling?
• Technical: Describe your experience with productionizing machine learning models in a regulated environment, including considerations for software engineering and model monitoring.
• Situational: Imagine you discover significant inconsistencies in longitudinal patient data streams affecting your model’s performance. How would you investigate and resolve this issue?
• Situational: Suppose a new advancement in medical foundational models emerges after your model is deployed. How would you evaluate and incorporate this innovation?
• Situational: If you receive conflicting priorities between research goals and product delivery timelines, how do you manage stakeholder expectations and project execution?
• Company Fit/Motivation: Why are you specifically interested in working at Verily rather than other AI or digital health companies?
• Company Fit/Motivation: How do your personal values and career aspirations align with Verily’s mission to transform precision health through data-driven solutions?
• Company Fit/Motivation: Verily operates at the intersection of technology and healthcare regulation. How do you stay informed and adapt your approach in such a dynamic space?
• Questions to Ask Interviewer: Can you describe how the Data Science team collaborates with clinical researchers and product teams to ensure AI solutions meet real-world healthcare needs?
• Questions to Ask Interviewer: What are the biggest technical and organizational challenges currently facing the generative AI initiatives at Verily?
• Questions to Ask Interviewer: How does Verily support continuous learning and career growth for data scientists working on cutting-edge healthcare AI projects?