Principal Data Scientist, Gen AI Foundation

Genentech • South San Francisco, CA • 7h ago

The Position

Why Genentech

We're passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world's most complex health challenges and transforming society.

Genentech's Data, Digital, and Analytics (DDA) team is dedicated to solving complex healthcare challenges and improving patient outcomes. DDA empowers business partners across Commercial, Medical, and Government Affairs (CMG) to make impactful decisions by leveraging data, analytics, and AI/ML to enable fast, targeted actions in rapidly evolving business @contexts.

DDA fosters a unified understanding of customers, actions, and outcomes by transforming the business insight supply chain from the traditional reactive service model to a modern proactive product model, which integrates analytics and insights seamlessly into CMG's evolving digital, data, and automation platforms, creating scalable solutions and eliminating silos. In DDA, you will work as a trusted, objective advisor and expert, recommending critical decisions and actions to be taken with credibility and a focus on driving measurable impact. You will be part of a diverse, inclusive team that reflects the world we serve, thriving in a welcoming culture built on collaboration and innovation.

The Opportunity

The Principal Data Scientist, GenAI Foundation leads back-end product development and is a critical technical role focused on building and scaling the next generation of agentic infrastructure within Genentech. This role drives the development of extensible multi-agent systems, ensuring robust governance and the transformation of traditional services into advanced agentic components. As a key technical influencer, you will stay at the forefront of Generative AI infrastructure, security, and observability, fostering deep collaborations across the global Roche organization to deliver innovative AI solutions that solve complex healthcare challenges.

Scalable Infrastructure Development: Design and build an extensible, multi-agent infrastructure and the foundational systems required to transform traditional services into high-performing agentic components.

Enterprise Governance & Security: Establish robust governance frameworks for agents and data sources, ensuring all infrastructure meets stringent security, observability, and compliance standards (Genentech/Roche).

Technical Innovation & Scouting: Stay at the forefront of Generative AI infrastructure and security, proactively integrating emerging technologies to maintain a competitive edge within a complex enterprise environment.

Strategic Technical Leadership: Influence key technical decisions and priorities, balancing high-level business goals with the need for scalable, reliable AI systems.

Organizational & Global Representation: Act as a primary ambassador for the team, fostering thriving relationships with internal and external partners across Genentech and the global Roche organization.

AI Mentorship & Coaching: Elevate the organization's technical maturity by coaching team members and stakeholders to become "AI-savvy" and fostering an innovation-led, "fail-forward" mindset.

Operational Integrity: Maintain a respectful, collaborative team culture while surfacing systemic issues to leadership and ensuring strict adherence to all laws, regulations, and corporate policies.

Who You Are

Bachelor's degree and 8+ years of experience in a relevant field.

Architectural Mastery & RAG: Expert-level command of RAG architectures and document optimization (chunking), supported by deep proficiency in vector and graph databases, SQL, and AI-generated SQL techniques.

Frameworks & Agentic Protocols: Hands-on experience with LLM frameworks (LangChain, LangGraph, AWS Agentcore) and a mastery of Gen AI tools, including MCP and A2A protocols for seamless system communication.

System Transformation & Integration: Proven ability to stay on the cutting edge of multimodal architectures, with the specific technical skill to transform traditional ML models and API services into agentic, MCP-compliant tools.

Observability & Performance Engineering: Comprehensive mastery of observability tools for agentic systems, with a focus on optimizing the critical trade-offs between cost, reliability, and latency.

Preferred

Experience in building infrastructure for Gen AI products in B2B environments.

Experience with multimodal Gen AI tools, especially voice-based interfaces.

Experience in security common protocols.

Experience in the healthcare, pharmaceutical, or highly regulated industries.

This position is based in South San Francisco, CA and offers a hybrid schedule working 3 days per week on campus.

Relocation benefits are not available for this posting.

The expected salary range for this position based on the primary location of South San Francisco, CA is $207,480 and $385,320. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits (https://roche.ehr.com/default.ashx?CLASSNAME=splash)

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants (https://docs.google.com/forms/d/e/1FAIpQLSdZWlsbfQOvFVIQgHE_iDzWUTlhZvj6FytIzjS7xq6IGh1H5g/viewform) .

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