Head, Automation Science & Technology, Autonomous Lab Accelerator

The Position

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

The Opportunity

The single automation strategy and implementation leader responsible for defining and driving the automation, robotics, and autonomous experimentation strategy across the Roche Autonomous Lab Accelerator programs, in partnership w/ gRED, pRED and PT laboratory science teams.

Sets the multi-year vision, prioritizes automation work streams, and directly informs senior leadership's budgetary decisions in alignment with organizational priorities. The role bridges wet-lab execution with dry-lab design and analysis to build a closed-loop, autonomous "lab-in-the-loop" (LiTL) across the Design–Make–Test–Analyze (DMTA) cycle.

This role is a direct enabler of gRED, pRED and PT objectives to transform drug discovery through full implementation of Lab-in-the-Loop, embedding data, AI, and experimentation to systematically improve speed, confidence, and probability of success. In support of the push toward Foundational Data for Foundational Models (FDFM), the role ensures automation and informatics are tightly integrated to generate the high-quality, structured datasets required to train and scale the discovery-enabling models used to predict and design next-generation therapeutics.

Leads a small dedicated team and partners with scientific subject matter experts, computational sciences, informatics, and external vendors to define, build, validate, and scale automated workflows.

In this role, you will:

• Full implementation of Lab-in-the-Loop:
  Provide the automation backbone to embed LiTL across pharma REDs and PT so AI and experimentation systematically improve speed, confidence, and PTS.
• Foundational data & discovery-enabling models:
  Deliver the clean, standardized, scaled datasets via integrated automation and informatics, to enable foundational data and discovery-enabling models.
• Agent-enabled predictive insights:
  Generate the high-quality experimental data that feeds agent-enabled predictive insights informing target validation decisions.
• Operational excellence & scale:
  Support automation and rollout of priority processes using AI and digital solutions, reduce operational friction, and improve cost efficiency and cycle time through technology-enabled ways of working.
• Everyday AI & high-performing organization:
  Champion adoption of AI tools and practices with measurable impact within Drug Discovery automation work streams.
• Aligning with R&D Excellence goals:
  Building and supporting AI/ML models for antibody generation, optimization, and LM-transition decisions.

• Strategy & Prioritization:
  Own the multi-year automation and autonomous experimentation strategy, implementation and sandbox lab operations; prioritize ongoing and planned work streams across pharma REDs and PT.
• Budget Influence & Financial Planning:
  Directly inform senior leadership budgetary decisions; build long-term spend models balancing fixed capital vs. lease/license; lead vendor negotiations and own vs. lease vs. license decisions; support objectives of improving cost efficiency through technology-enabled ways of working.
• Lab-in-the-Loop:
  Architect the closed-loop…