Director, Computational Biology

Responsibilities

• Lead the development of a target identification and prioritization pipeline using multi-omics data (transcriptomics, proteomics, metabolomics, chromatin conformation, and functional genomics – e.g. CRISPR screens).
• Define internal standards for validation of target discovery methods, including benchmarking against orthogonal datasets.
• Apply rigorous model-to-human translation, evaluating cross-species conservation, model fidelity, and translational risk when nominating and de-risking targets.
• Partner with the Human Genetics group to integrate genetic evidence into target identification and prioritization.
• Propose targets with clear mechanistic hypotheses, building evidence packages that draw on orthogonal data and analyses.
• Audit data quality, batch effects, and biological confounders throughout the analytical cycle – statistical rigor and reproducibility are paramount.
• Develop and maintain scalable, reproducible analytical pipelines, including human-in-the-loop-AI workflow as required. Use best practices in software engineering and ensure appropriate data governance and infrastructure for large-scale omics data.
• Lead or contribute to cross-functional target assessment documents and portfolio decision-making meetings with clear, evidence-based scientific recommendations.
• Lead a team of computational biologists fostering a culture of scientific rigor, reproducibility, and continuous learning.
• Maintain working knowledge of the current literature on computational biology and stay current with novel methodologies, including applicable advances in AI.

Requirements

• PhD in computational biology, bioinformatics, genomics, systems biology, or a closely related quantitative field and 8+ years of relevant experience required.
• Proven experience in target identification and validation using multi-omics approaches in a pharma or biotech.
• Experience in multi-omics analyses and integration (including RNA-seq, ATAC-seq – bulk, single cell – spatial transcriptomics, proteomics, metabolomics, ChIP-seq and similar, long read RNA-seq, small RNA-seq, and functional genomics perturbation screens).
• Methodological breadth across comp-bio modeling spectrum – classical and hierarchical statistics, causal inference, Bayesian modeling, network-based and mechanistic approaches, and modern deep learning – with hands-on depth in biological foundation models (fine-tuning, interpretability, efficient deployment) and the judgment to match method to biological question and data.
• Proficiency in Python and/or R, plus working knowledge of Bash and Docker is required, proficiency or familiarity with Nextflow workflows and AWS cloud stack strongly preferred.
• Knowledge of human genetic analyses methods (e.g. GWAS, rare variant analyses, QTL colocalization etc.) is preferred.
• Experience directly managing PhD-level scientists is required.
• Excellent communication skills across audiences, with a track record of influencing cross-functional teams (including non-computational partners) and translating complex computational findings into clear, actionable recommendations that inform senior leadership on portfolio and target decisions.

Qualifications

• PhD in computational biology, bioinformatics, genomics, systems biology, or a closely related quantitative field and 8+ years of relevant experience required.

Skills

• PhD in computational biology, bioinformatics, genomics, systems biology, or a closely related quantitative field and 8+ years of relevant experience required.
• Proven experience in target identification and validation using multi-omics approaches in a pharma or biotech.
• Experience in multi-omics analyses and integration (including RNA-seq, ATAC-seq – bulk, single cell – spatial transcriptomics, proteomics, metabolomics, ChIP-seq and similar, long read RNA-seq, small RNA-seq, and functional genomics perturbation screens).
• Methodological breadth across comp-bio modeling spectrum – classical and hierarchical statistics, causal inference, Bayesian modeling, network-based and mechanistic approaches, and modern deep learning – with hands-on depth in biological foundation models (fine-tuning, interpretability, efficient deployment) and the judgment to match method to biological question and data.
• Proficiency in Python and/or R, plus working knowledge of Bash and Docker is required, proficiency or familiarity with Nextflow workflows and AWS cloud stack strongly preferred.
• Knowledge of human genetic analyses methods (e.g. GWAS, rare variant analyses, QTL colocalization etc.) is preferred.
• Experience directly managing PhD-level scientists is required.
• Excellent communication skills across audiences, with a track record of influencing cross-functional teams (including non-computational partners) and translating complex computational findings into clear, actionable recommendations that inform senior leadership on portfolio and target decisions.

Benefits

Full Benefits Link: https://ionis.com/careers#:~:text=Highly%20competitive%20benefits

Pay

$200,385 to $250,332