Principal Data Scientist - Agentic AI Discovery & Prototyping

Genentech•Daly City, CA

4d•Onsite

About The Position

The Principal Data Scientist - Agentic AI Discovery & Prototyping will play a foundational role in building and advancing Genentech’s AI Innovation capabilities within the AI Management, Governance, and Research (MGR) team. This role is responsible for exploring frontier AI capabilities, rapidly translating research breakthroughs into experimental prototypes, and systematically advancing validated innovations into the enterprise AI product ecosystem. Operating at the intersection of AI research discovery, advanced experimentation, and applied product development, this role will explore emerging techniques across Generative AI, predictive modeling, and causal inference, with particular emphasis on large language models, multi-agentic human-in-the-loop systems, and advanced AI tooling ecosystems. The role will focus on identifying high-potential innovations through structured opportunity sensing, designing innovation sprint cycles, and building experimental AI systems that test new architectures, models, and frameworks. This includes evaluating emerging approaches such as agentic orchestration, model fine-tuning, tracing-auditing, alignment strategies, AI measurement strategies, multimodal AI workflows, and advanced model evaluation frameworks. Through a formal R&D operating model, the Principal Data Scientist will lead experimentation initiatives defined by structured research briefs outlining hypotheses, system architectures, evaluation methodologies, and scalability pathways. Successful prototypes will inform and accelerate downstream product development in partnership with AI Product Management, Data Science, and ML Engineering teams. In parallel, this role contributes to shaping the organization’s AI research and innovation ecosystem, including producing internal AI Signals Briefs, evaluating emerging technologies, and identifying opportunities for technical publications, patents, and reusable AI capabilities that strengthen Genentech’s long-term AI leadership. This role requires a strong blend of technical depth, hands-on prototyping ability, and systems thinking, enabling Genentech to responsibly explore emerging AI capabilities while maintaining strong alignment with enterprise priorities, governance frameworks, and real-world product impact.

Requirements

Bachelor’s degree with 10+ years of experience in Data Science, Machine Learning, Artificial Intelligence, or a related field, or Master’s/PhD with 7+ years of experience.
Demonstrated experience working in AI research, advanced experimentation, or applied ML innovation environments.
Generative AI & Advanced AI Systems
Deep hands-on experience developing Generative AI systems, including techniques such as:
Retrieval-augmented generation (RAG)
Prompt engineering and prompt optimization
Model fine-tuning and alignment
Agentic AI systems or multi-agent workflows
Experience working with modern LLM ecosystems and orchestration frameworks.
Thought leadership in Responsible AI principles
Machine Learning & Causal Methods
Strong foundation in Machine Learning and Predictive Modeling techniques.
Experience designing experiments that evaluate model behavior and AI system performance.
Rapid Prototyping & AI System Development
Demonstrated ability to translate emerging research into working prototypes and experimental systems.
Experience building AI applications using modern development frameworks and APIs.
Technical Stack
Strong programming skills in Python and familiarity with modern AI frameworks such as:
PyTorch
TensorFlow
Hugging Face
LangChain / LlamaIndex / LangGraph or similar
Experience working with vector databases, embedding models, and LLM application architectures.
Demonstrated experience using AI assisted coding tools and IDEs such as Cursor, Claude Code, or Codex.
Evaluation & AI Systems Thinking
Experience designing model evaluation frameworks, benchmarking pipelines, or experimentation harnesses.
Understanding of system-level tradeoffs including latency, reliability, cost efficiency, safety, and scalability.
Communication
Strong written and verbal communication skills with the ability to translate complex AI concepts into clear technical guidance for diverse audiences.

Nice To Haves

Working knowledge of AI protocols like MCP and A2A in the context of multi-agent orchestration systems.
Experience working with synthetic data generation, model evaluation datasets, or automated testing frameworks for AI systems.
Track record of contributing to research publications, patents, or open-source AI projects.
Experience working within innovation labs, applied research teams, or emerging technology R&D environments.
Experience in healthcare, life sciences, or other highly regulated industries.

Responsibilities

AI Frontier Research Opportunity Sensing
Continuously monitor and synthesize advancements across academic research, open-source innovation, and industry developments in areas such as Generative AI, Causal Inference, Predictive Modeling, and AI System Architectures.
Translate emerging research signals into practical experimentation hypotheses and innovation opportunities aligned with Genentech’s strategic AI priorities.
Contribute to recurring AI Signals Briefs that summarize key research papers, technical blogs, open-source Github repositories, and emerging tools that may influence the enterprise AI roadmap.
Rapid Prototyping & Innovation Sprints
Design and execute innovation sprint cycles that rapidly evaluate emerging AI capabilities through experimental prototypes and proof-of-concept systems, grounded in real-world business objectives.
Build working prototypes leveraging techniques such as:
Agentic AI systems and multi-agent orchestration frameworks
Retrieval-augmented generation (RAG) architectures
Model fine-tuning and alignment approaches
Multimodal AI pipelines
AI-assisted automation and knowledge agents
Ensure experimentation initiatives maintain a clear connection to the AI product roadmap, accelerating the transition from concept to validated capability.
Experimental Design & AI Evaluation Frameworks
Establish R&D experimentation frameworks including research briefs that define hypotheses, architectures, evaluation metrics, and scalability considerations.
Develop model evaluation harnesses and benchmarking pipelines for assessing LLMs and AI systems across dimensions such as performance, hallucination risk, reliability, latency, and cost.
Design evaluation approaches for agentic systems, RAG pipelines, and complex AI workflows using structured experimentation methodologies.
AI Systems Architecture & Capability Development
Prototype reusable AI architectures and system patterns that can accelerate enterprise AI development.
Evaluate emerging frameworks related to:
LLM orchestration
Multi-agent and Autonomous agents in a regulated environment
LLMOps and evaluation tooling
AI development platforms and model serving infrastructure
Explore techniques such as synthetic data generation, automated evaluation pipelines, and AI-assisted experimentation workflows.
Collaboration with Product & Engineering
Partner with AI Product Management to translate validated prototypes into roadmap-ready capabilities.
Collaborate with Data Science and ML Engineering teams to transition experimental systems into scalable production architectures.
Provide technical thought leadership on emerging modeling approaches, tooling ecosystems, and AI experimentation methodologies.
Knowledge Management & AI Thought Leadership
Document experimental insights, system architectures, and evaluation results to build a structured AI research knowledge base.
Help define and evolve the organization’s AI innovation focus domains (e.g., agentic AI systems, model optimization, AI tooling ecosystems).
Identify opportunities for technical publications, internal research briefs, and intellectual property generation emerging from innovation initiatives.
Compliance
Comply with all laws, regulations and policies that govern the conduct of Genentech activities.
People
AI Coaching: Coach junior team members and stakeholders to become more AI-savvy.
Leadership Insight: Surface potential systemic issues to the leadership of the team.
Relationship Building: Maintain a respectful and constructive relationship with the partnering teams.
Agile Mindset: Be willing to take risks, fail forward, and compromise based on the business priorities.