Senior Manager, Advanced Technology & Automation

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Laboratory Setting, Office

Job Description

As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.

About the Role

Thermo Fisher Scientific’s Genetic Sciences Division R&D is developing the technologies needed for the next generation of genetic-analysis platforms and automated scientific workflows.

We are seeking a Senior Manager of Advanced Technology & Automation to lead the incubation, prototyping and maturation of emerging technologies across instrumentation, consumables, molecular biology and laboratory automation.

Working within the architecture and technology direction established for the division, this leader will turn promising ideas into evidence-based system capabilities. The role will build a team that experiments rapidly, resolves critical uncertainties and transitions successful technologies into product-development organizations.

The successful candidate will combine scientific curiosity, hands-on engineering and disciplined execution. This person must be comfortable making investment and termination decisions based on customer value, technical evidence, scalability and commercial potential.

What You’ll Do

Lead Advanced Technology Incubation

• Build and manage a focused portfolio of advanced technology and automation initiatives.
• Translate customer needs, scientific trends and technology opportunities into clear hypotheses and learning objectives.
• Lead feasibility studies, prototypes and experiments that address the most important technical and commercial uncertainties.
• Establish staged maturation plans from exploration through feasibility, system validation and product transition.
• Define evidence-based criteria for continuing, redirecting or stopping initiatives.
• Balance rapid experimentation with appropriate scientific rigor, documentation and intellectual-property protection.
• Ensure incubation investments remain aligned with platform architecture and business strategy.

Develop Next-Generation Technologies

• Advance technologies across areas such as:

• Genetic analysis and multiomics workflows
• Molecular biology and sample preparation
• Microfluidics and consumable architectures
• Optical detection and precision instrumentation
• Sensors, embedded systems and intelligent control
• Laboratory automation, robotics and connected workflows
• AI-enabled experimentation and data-driven systems

• Integrate scientific, consumable, instrument, software and data considerations from the beginning.
• Build prototypes that demonstrate system and customer value—not isolated technical performance.
• Identify scalability, manufacturability, reliability and regulatory constraints early.
• Partner with system architects to ensure new capabilities align with reusable platform foundations.
• Convert experimental learning into technology roadmaps, technical requirements and product recommendations.

Advance Automation and Lab-in-the-Loop

• Develop automated sample-to-answer and multi-instrument workflows.
• Integrate instruments with liquid handlers, robotics, workflow orchestration and laboratory data systems.
• Build Lab-of-the-Future concepts that improve throughput, reproducibility and ease of use.
• Develop Lab-in-the-Loop capabilities using experimental data and real-time feedback to adapt or optimize workflows.
• Partner with customers and automation vendors to test representative scientific applications.
• Ensure automation concepts can evolve into scalable, supportable product capabilities rather than one-off demonstrations.

Apply AI to Science and Engineering

• Identify high-value applications of AI in experimental design, workflow optimization, anomaly detection, intelligent control and data interpretation.
• Use AI-assisted methods to accelerate concept exploration, modeling, experiment planning and technical analysis.
• Develop data and system foundations required for reliable AI-enabled capabilities.
• Partner with data science, software and domain experts to prototype and evaluate AI-enabled workflows.
• Ensure appropriate attention to data quality, robustness, traceability, cybersecurity, validation and human oversight.
• Measure AI initiatives through scientific, customer and engineering outcomes—not tool adoption alone.

Transition Technology into Products

• Partner with Product Management, architecture and product-development teams to define transition pathways.
• Demonstrate technical feasibility, customer value and architecture alignment before product handoff.

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