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We are seeking a highly motivated Research Scientist – Proteomics Workflow Innovation (m/f/d) with strong practical expertise in LC–MS-based proteomics and a passion for data analysis, interpretation, and generation of biological insight. This role is ideal for a scientist who combines experimental expertise with the ability to translate complex data into meaningful conclusions and drive innovation in proteomics workflows. The successful candidate will play a key role in designing, developing, and benchmarking next-generation proteomics and secretomics workflows across diverse biological systems, while leading the interpretation of complex datasets. This includes establishing robust, scalable, and reproducible workflows that deliver high-quality data and enable biologically meaningful insights. The role requires a strong data-first mindset, ensuring that workflows are optimized for downstream analysis and decision-making.
Job Responsibility
Design, develop, and benchmark innovative LC–MS-based proteomics and secretomics workflows across diverse biological systems
Evaluate and implement fit-for-purpose methods based on performance metrics such as data quality, reproducibility, and biological relevance
Establish robust, scalable, and reproducible workflows for high-quality data generation
Drive end-to-end understanding of proteomics datasets, from experiments to biological interpretation and hypothesis generation
Partner with computational and data science teams to ensure appropriate data processing, accessibility, and usability
Translate experiments into standardized and scalable workflows
Contribute to documentation (SOPs, reports) to ensure reproducibility and knowledge sharing
Collaborate across multidisciplinary matrix teams and communicate scientific findings effectively to both technical and non-technical stakeholders
Requirements
MSc or PhD in Biochemistry, Proteomics, Analytical Chemistry, or related discipline
Hands-on experience in LC–MS-based proteomics workflows
Experience working at the interface of wet-lab and computational analysis
Demonstrated experience in data analysis and interpretation of proteomics datasets
Strong ability to connect experimental data to biological context and hypothesis generation
Excellent problem-solving and communication skills
Programming and data analysis skills (e.g., Python, R) and familiarity with AI/ML are desirable
Experience with secretomics or extracellular proteomics workflows is preferred
Exposure to metabolomics workflows and multi-omics data integration is advantageous
Nice to have
Programming and data analysis skills (e.g., Python, R) and familiarity with AI/ML are desirable
Experience with secretomics or extracellular proteomics workflows is preferred
Exposure to metabolomics workflows and multi-omics data integration is advantageous