Senior Computational Biologist

POSITION SUMMARY

Our Inflammation & Immunology (I&I) unit is dedicated to developing transformative treatments for patients with autoimmune, inflammatory, and immune-mediated diseases, by understanding the pathogenic mechanisms underlying inflammation. As a Senior Computational Biologist in our Systems Immunology group, you will design and implement robust data pipelines that ensure the integrity, scalability, and reproducibility of multi-omics analyses—enabling seamless integration of genomic, transcriptomic, and proteomic data. By applying best practices in data engineering and reproducible research, you will help transform raw biological data into actionable knowledge, accelerating target discovery, biomarker identification, and indication selection, across the drug development pipeline.

RESPONSIBILITIES

• Develop and maintain robust workflows for integrating diverse datasets including genomics, transcriptomics, proteomics, spatial omics, and functional genomics studies.

• Apply best practices in data management and curation; ensure data integrity, traceability, and version control.

• Promote reproducibility and transparency in computational research through standardized methods and documentation.

• Communicate methods, analytical results, and interpretation, effectively to collaborators and leadership.

• Lead and participate in project teams to mine data for new targets and inform precision medicine strategies.

MINIMUM QUALIFICATIONS

• Ph.D. in Computational Biology, Bioinformatics, Biostatistics, or a related field with relevant experience applying quantitative methods to biological questions OR

• Master’s degree with at least 3 years of relevant experience applying quantitative methods to biological questions in a pharmaceutical industry setting.

• Expertise in computational genetics and multi-omics data analysis, including the integration of genomic, transcriptomic, proteomic, spatial, and other high-dimensional datasets to uncover disease mechanisms and inform therapeutic strategies.

• Proficiency in Python and R for data analysis, with working knowledge of Linux-based systems, high-performance computing (HPC) environments, and cloud computing platforms (e.g., AWS, GCP, or Azure) to support scalable and reproducible research workflows.

• Demonstrated experience in organizing and managing complex biological datasets using reproducible research practices, including version-controlled workflows, standardized documentation, and adherence to data governance principles to ensure transparency, scalability, and scientific rigor.

PREFERRED QUALIFICATIONS

• Background in human biology or medicine, especially in immunology & inflammation.

• Familiarity with public or proprietary databases related to inflammatory diseases.

• Experience with statistical genetics, population genetics and medical genetics.

• Experience with integrating across multi-omics data types, with clinical data and real-world data.

• Experience implementing NextFlow Pipelines using AWS Batch.

• Experience using PerturbSeq data to infer causal gene regulatory networks, and/or virtual cell foundation models.

• Experience with single-cell epigenetics and/or DNA foundation models to integrate locus-to-transcript-to-phenotype relationships at scale.

• Strong quantitative skills in statistical and machine learning methods.

Relocation support available

Work Location Assignment: Hybrid

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