Job Description

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Able to lift 40 lbs. without assistance, Laboratory Setting, Office, Some degree of PPE (Personal Protective Equipment) required (safety glasses, gowning, gloves, lab coat, ear plugs etc.), Strong Odors (chemical, lubricants, biological products etc.)

Thermo Fisher Scientific is seeking an experienced Bioinformatician / Data Scientist to support the development and implementation of AI-enabled workflows for bead surface design, bioconjugation technologies and proteomics applications.

How you will make an impact:

This is a strategic, cross-functional role at the interface between data science, chemistry, biology, assay development and product innovation. The role will support Thermo Scientific projects aimed at developing AI-enabled approaches for predicting and optimizing bead designs for proteomics workflows and diagnostic assays. The position will be central in translating AI/ML model outputs into practical R&D insight, enabling faster product development, improved first-pass success in customer projects and stronger data-driven decision-making across Thermo Fisher Scientific.

The successful candidate will be a key contributor to Thermo Fisher Scientific's strategic AI initiative, helping move bead and proteomics development from wet-lab driven optimization toward in silico-guided design. This role will help establish new AI/ML workflows from the ground up, translating experimental bead chemistry, proteomics and bioconjugation data into model-ready datasets and actionable design insights. The candidate will work closely with internal R&D teams, Thermo Fisher Scientific's AI/ML experts and external partners to build, test and implement models that accelerate product development and strengthen data-driven decision-making.

This is an opportunity for someone who enjoys working at the frontier between experimental science and computational methods — creating something new, solving complex scientific problems and helping turn AI/ML from concept into practical R&D tools.

What you will do:

You will work closely with scientists in bead chemistry, proteomics, assay development, automation and AI/ML modelling to:

• Lead the implementation and further development of AI/ML workflows for bead surface design and assay performance prediction
• Translate biological, chemical and assay-related data into model-ready datasets
• Develop predictive models, statistical modelling and data-driven decision tools to support R&D and product development
• Solution AI/ML algorithms for experimental design, bead selection, coupling strategy recommendations and customer-specific assay optimization
• Lead data structuring, metadata definition, database use and data quality processes
• Collaborate with AI/ML experts to evaluate model performance, uncertainty, interpretability and practical relevance for R&D decision-making
• Act as a bridge between computational partners and Thermo Fisher Scientific's experimental R&D teams
• Lead the integration of AI-enabled workflows into existing product development and customer support processes
• Support knowledge transfer and capability building within data science, AI/ML and bioinformatics across the R&D organization

What we offer:

This role offers a unique opportunity to contribute to the next generation of AI-enabled bead and proteomics workflows at Thermo Fisher Scientific. You will be part of a highly skilled R&D environment with deep expertise in Dynabeads, surface chemistry, assay development, proteomics and product innovation. This role will contribute directly to strategic innovation activities and help build long-term capabilities in AI-enabled product development.

You will have the opportunity to influence how data science and AI/ML are applied in industrial R&D, working on technologies that support diagnostics, biomarker discovery, proteomics and life science research globally.

REQUIREMENTS

How you will get here:

We are looking for an experienced candidate with a strong scientific background and the ability to operate strategically across disciplines.

Education and experience:

• MSc or PhD in bioinformatics, data science, computational biology, biostatistics, biochemistry or a related field
• Experience with AI/ML, statistical modelling or predictive modelling in a life science, biotechnology, diagnostics or chemistry-related context
• Strong programming skills in Python and/or R, with experience handling scientific datasets
• Good understanding of data quality, metadata, data structuring, databases and scientific data management
• Ability to translate complex scientific questions into data science workflows, model-ready datasets and actionable recommendations

Other relevant experience:

• Experience with model evaluation, uncertainty estimation, design of experiments, multivariate analysis or low-data modelling
• Experience with generative AI technologies, LLM-based workflows or modern AI tools for scientific productivity
• Experience with cloud-based data platforms, workflow orchestration, MLOps, ELN/LIMS systems or laboratory data infrastructure
• Experience integrating AI/ML workflows into laboratory automation, experimental workflows or product development environments
• Knowledge of proteomics, protein chemistry, antibody-based assays, bioconjugation, bead technologies, bioprocessing or biologics manufacturing, and/or experience from industrial R&D in diagnostics, biotechnology, pharmaceuticals or life science tools

Personal attributes:

We are looking for someone who combines scientific depth with strategic thinking and strong collaboration skills. The ideal candidate is curious, structured and able to work effectively across disciplines. You should be comfortable working in an environment where biology, chemistry, data science and product development meet. You do not need to be an expert in every area, but you should be able to understand complex scientific challenges, ask the right questions and help turn data into insight.

You are likely to succeed in this role if you:

• Enjoy working at the interface between experimental science and computational methods
• Communicate clearly with both data scientists and laboratory scientists
• Can translate AI/ML results into practical R&D decisions
• Are motivated by applying data science to real industrial and diagnostic challenges
• Have a strategic mindset and can shape how AI/ML is used in future product development
• Thrive in cross-functional collaboration and knowledge-sharing