Scientific Manager- Multi-Omics & Target Prioritisation

Excelra

Bioinformatician – Multi-Omics & Target Prioritisation

Location

Europe

Hybrid

Excelra is a leader in biopharma data and analytics, that uniquely blends scientific understanding with technological expertise. This dual mastery empowers innovative drug discovery and development, transforming research data into actionable insights for groundbreaking discovery.
We have locations in Hyderabad (head offices – India), Ghent (Belgium), London (UK), Utrecht (The Netherlands), and Boston/SFO (US).
Website:

www.excelra.com

About the role

We are hiring a

to support the development of a platform focused on understanding the molecular mechanisms of biological ageing and identifying promising targets for future research and intervention.
This role combines hands-on bioinformatics, multi-omics data integration, biological ageing analysis, and AI-enabled target discovery. You will work closely with scientists and multidisciplinary teams to integrate large-scale molecular datasets, evaluate biological ageing signatures, and develop computational approaches to prioritize targets with potential relevance to healthy ageing.
You will play a key role in transforming diverse omics datasets into actionable biological insights through reproducible workflows, advanced analytics, and AI-driven evidence assessment.

Key Responsibilities

Identify and assess multi-omics datasets that can be reused to study biological ageing, with a particular focus on epigenomics, proteomics, and transcriptomics.
Develop workflows to bring together data from different studies and omics technologies, including data acquisition, harmonisation, and quality control.
Apply and benchmark different biological ageing clocks to understand how well they perform across datasets and molecular data types.
Integrate multi-omics data to identify molecular features and biological processes associated with faster or slower ageing using approaches such as mixOmics.

Develop and maintain AI-agentic workflows that help annotate, summarise evidence, score, prioritise, and generate hypotheses around candidate targets identified above.
Develop approaches for prioritising targets based on the strength of the available evidence, their biological relevance, and their potential for further research.
Work closely with a multidisciplinary team to review the most promising targets and assess areas such as biological plausibility, mechanism, therapeutic potential, and safety.
Present computational findings and supporting evidence to the wider scientific team to help guide target selection and downstream experimental work.

Analyze large-scale biological datasets and translate analytical outputs into meaningful biological insights.
Support the interpretation of molecular signatures, pathways, and biological processes associated with ageing and longevity.
Contribute to the development of reproducible analytical workflows and best practices for omics data analysis.
Maintain thorough documentation and ensure reproducibility of computational analyses.

Collaborate closely with scientists, bioinformaticians, and data stakeholders to understand research objectives and translate them into practical analytical approaches.
Communicate analytical results and scientific findings clearly to both technical and non-technical audiences.
Contribute to ongoing scientific discussions and support data-driven decision-making for target selection and research prioritisation.

A PhD in Bioscience engineering, Bioinformatics, computer science, Computational Biology, Data Science, Computer Science, Genomics, Systems Biology, or a related discipline– or an MSc with 5+ years of equivalent experience. You can demonstrate this through peer-reviewed publications, contributions to public code repositories or relevant project work.
Experience working with omics datasets, including transcriptomics, proteomics, epigenomics, or related molecular data types.
Hands-on experience with multi-omics data integration and biological data analysis.
Strong programming skills in Python and/or R.
Experience developing reproducible bioinformatics workflows and analytical pipelines.
Understanding of statistical analysis and data quality control methodologies for biological datasets.
Familiarity with biological ageing research, molecular biomarkers, or related biological processes.
Strong problem-solving and analytical skills.
Excellent communication and collaboration skills.
Ability to work independently while partnering effectively with multidisciplinary teams.

Experience applying biological ageing clocks, epigenetic clocks, or related ageing biomarkers.
Experience with multi-omics integration frameworks such as mixOmics or similar methods.
Experience developing AI-assisted or agent-based workflows for scientific research.
Familiarity with target discovery, target prioritisation, or translational research workflows.
Experience with machine learning approaches applied to biological data.
Experience working with cloud-based or HPC computing environments.
Experience with SQL databases and scientific data management platforms.
Experience with Git and collaborative software development practices.
Experience supporting research, biotechnology, pharmaceutical, or life sciences environments.

A challenging job environment with advancement opportunities.
Flexible hybrid working in an international setting.
A dynamic company with an open culture, easy communication, and an ambitious vision.
A rich and global team with a mix of backgrounds, experiences, and expertise.
Independence and flexibility.
Opportunities to work on large-scale genomics datasets and cutting-edge sequencing technologies.
An attractive salary package with extra-legal benefits.