At a glanceSummarised by Seekless from the posting.
Must have12
PhD or Postdoc with 0-3 years of experience in Computational Biology, Bioinformatics, Statistical Genetics
Proficiency in building pipelines and analyzing multi-modal Omics data (Bulk RNAseq, scRNAseq, Proteomics, ATACseq, Microarray, Spatial transcriptomics)
Ability to communicate complex ideas clearly and concisely
Correlate results, generate hypothesis and scientific insights for applications in biomarker identification, indication selection and patient stratification
Proficient in R (including Shiny) and Python
Experienced with cloud computing (AWS S3, EC2, SageMaker)
Proficiency in Nextflow, Snakemake, WDL, CWL, AWS HealthOmics
Demonstrate expertise in quantitative statistics and data modeling (regression, linear models, hypothesis testing)
Knowledge of public databases (GEO, TCGA, GTEx, MsigDB, GWAS Catalog, CCLE, OpenTargets) and applications in drug discovery
Familiar with large biobanks (UK Biobank, FinnGen), GWAS, PheWAS, proteomics data, and HPC clusters, Docker, or Snakemake
Creative problem-solver and quick learner
Strong collaborator with organizational skills to drive projects toward team goals
Nice to have1
Therapeutic Area exposure in Oncology, Immunology or Neuroscience
Skills
R
Shiny
Python
AWS S3
AWS EC2
AWS SageMaker
Nextflow
Snakemake
WDL
CWL
AWS HealthOmics
Docker
GEO
TCGA
GTEx
MsigDB
GWAS Catalog
CCLE
OpenTargets
Position Overview
We are looking for experienced Computational Biologists and Bioinformaticians with a strong scientific background and understanding of real-world applications in drug discovery and development. Someone that can build scientific relationships, understand industry challenges and shape our Bioinformatics Solutions, working in collaboration with our global Bioinformatics, Data Science and Cloud teams.
This is an exciting opportunity to join our Global Bioinformatics Team to help leading Pharmaceutical and Biotech customers deploy and support cutting edge Bioinformatics Solutions and Technology globally.
Responsibilities
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Understand current and emergent problems, needs, requirements analysis, of the customers.
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Engage the scientific community (Genetics, Discovery Biology, Translational Genomics), taking insights and learnings from customers, scientific literature, conferences to shape, commercialize, and continuously evolve bioinformatics services aligned with emerging technologies and therapeutic priorities.
Apply and integrate bioinformatics pipelines, statistical modeling, machine learning/AI methods, and relational data systems to analyze heterogeneous internal and external datasets, enabling hypothesis generation and decision‑making in drug discovery and translational research.
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Research, assess, recommend, and integrate various data types to drive programs and clinical decisions.
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Contribute hands‑on to critical scientific initiatives and deliveries, while mentoring junior team members via technical guidance, rigorous scientific review, and workflow standardization.
Qualification & Background
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PhD or Postdoc with 0-3 years of experience in Computational Biology, Bioinformatics, Statistical Genetics.
•
Proficiency in building pipelines and analyzing multi-modal Omics data (Bulk RNAseq, scRNAseq, Proteomics, ATACseq, Microarray, Spatial transcriptomics).
•
Ability to communicate complex ideas clearly and concisely. Correlate results, generate hypothesis and scientific insights for applications in biomarker identification, indication selection and patient stratification.
•
Having Therapeutic Area exposure in the areas Oncology, Immunology or Neuroscience is a plus.
Requirements
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Programming & Computing - Proficient in R (including Shiny for interactive visualization) and Python; experienced with cloud computing (AWS S3, EC2, SageMaker).
•
Pipeline and workflow development - Proficiency in Nextflow, Snakemake, WDL, CWL, AWS HealthOmics.
•
Demonstrate expertise in quantitative statistics and data modeling, including regression, linear models, and hypothesis testing.
•
Knowledge and understanding of public databases & tools (GEO, TCGA, GTEx, MsigDB, GWAS Catalog, CCLE, OpenTargets et al), applications in drug discovery, analysis and integration of multi-omics & biobank data.
•
Are familiar with (or eager to work with) large biobanks (UK Biobank, FinnGen), GWAS, PheWAS, proteomics data, and computationally intensive environments like HPC clusters, Docker, or Snakemake.
•
Are a creative problem-solver and quick learner who remains productive when dealing with ambiguity and experimenting with new approaches.
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Act as a strong collaborator with the organizational skills and initiative needed to drive projects toward team goals.