Director, Computational Biology

Ionis Pharmaceuticals, Inc.•Boston, MA

40d•Hybrid

About The Position

We are seeking an exceptional computational biologist to lead target identification and multi-omics research efforts. In this role, you will drive the use of high-throughput, transcriptomics, epigenomics, proteomics, and functional genomics data to identify and prioritize novel therapeutic targets. You will bring a strategic, industry-wide perspective on the field — staying ahead of where it is moving and translating that into a differentiated approach which will align with Ionis's RNA-targeting and oligonucleotide platforms. You will lead a team of computational biologists, shape analytical strategy, and partner tightly with biology, human genetics, and drug discovery colleagues to bring forward the next wave of therapeutic targets.

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.

Nice To Haves

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.

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.
Assess 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.