Scientist/Senior Scientist, Computational Biology

About the role

Volastra Therapeutics, Inc. is a clinical-stage oncology biotech company focused on novel approaches to treat cancer by targeting chromosomal instability (CIN).

Responsibilities

• Lead bioinformatics analyses across discovery sciences and translational sciences, including RNA-seq, single-cell RNA-seq, whole-exome sequencing, whole-genome sequencing, copy number, mutation, structural variant, CRISPR screen, proteomic, or other omics datasets as appropriate.
• Develop reproducible, well-documented computational workflows for quality control, processing, feature engineering, integrated analysis, visualization, and reporting.
• Integrate internal experimental datasets with public cancer resources such as TCGA, DepMap, CCLE, CPTAC, cBioPortal, and relevant disease-specific cohorts to prioritize targets and biomarkers.
• Build analyses that connect cancer genotype, lineage, CIN biology, dependency, perturbation response, and therapeutic hypotheses.
• Partner with discovery teams to design experiments, interpret results, refine hypotheses, and identify the next best biological test.
• Support translational strategy by connecting preclinical models, patient genomics, and biomarker hypotheses. Work with clinical colleagues when analyses intersect with clinical samples, patient selection concepts, or exploratory biomarker readouts.
• Translate complex data into clear recommendations for project teams and leadership, with concise visualizations and transparent assumptions.
• Build durable code: modular Python and/or R packages, workflow management, data provenance, testing, version control, documentation, containers, and cloud or HPC execution.
• Use AI coding assistants and LLM-based tools to accelerate code generation, refactoring, unit-test drafting, documentation, pipeline scaffolding, and exploratory analysis. Treat AI output as draft code that must be reviewed, tested, documented, and validated.
• Help define technical standards for reproducible bioinformatics across the organization and mentor colleagues as appropriate.

Requirements

• PhD or equivalent experience in bioinformatics, computational biology, genomics, computational oncology, systems biology, bioengineering, computer science, or a related quantitative life-science field.
• Deep hands-on experience analyzing high-throughput sequencing data, especially transcriptomic and cancer genomic data.
• Strong coding ability in Python and/or R, with Unix/Linux and bash proficiency.
• Familiarity with cloud or HPC environments and scalable handling of large biological datasets.
• Biological insight in oncology, cancer genomics, chromosomal instability, DNA damage response, synthetic lethality, targeted therapy, or drug resistance.
• Ability to communicate complex analyses to biologists, chemists, translational scientists, clinicians, and leadership with clarity and judgment.
• High scientific integrity, intellectual ownership, and comfort working in a fast-moving, hypothesis-driven team.
• Experience in target discovery, biomarker discovery, patient population mapping, translational genomics, or computational oncology.
• Experience analyzing functional genomics screens, Perturb-seq, CRISPR dependencies, cell line or organoid datasets, pharmacogenomic response data, or multi-omics perturbation studies.
• Experience building reusable internal tools, structured analysis reports, or lightweight dashboards for project teams.
• Familiarity with clinical genomics assay outputs and exploratory biomarker workflows.
• Track record of collaborating with wet-lab scientists to turn computational hypotheses into experiments and decisions.
• Experience using AI coding assistants, code agents, or LLM-based developer tools in a way that improves quality, speed, testing, and documentation.

Benefits

Volastra offers a competitive salary range of approximately $140,000 - $180,000, which may vary depending on qualifications, experience, and ultimate leveling. Leveling outside of the stated range may be considered for exceptional candidates on a case-by-case basis.