Scientist/Senior Scientist, Computational Biology

Volastra Therapeutics•New York, NY

1d•$140,000 - $180,000•Onsite

About The Position

Volastra Therapeutics, Inc. is a clinical-stage oncology biotech company based in New York City, pioneering novel approaches to treating cancer by targeting chromosomal instability (CIN), a unique vulnerability in cancer. Since its founding in 2019, Volastra has grown to support ongoing discovery efforts and a growing clinical organization. Our lead pipeline programs focus on two distinct inhibitors of KIF18A, a novel therapeutic target. VLS-1488, internally discovered, entered clinical trials in Q4 2023 for the treatment of advanced cancers. Sovilnesib, in-licensed from Amgen in 2023, re-entered the clinic in Q2 2024. Both assets have been granted Fast-Track Designation by the FDA. Volastra is also developing new techniques to understand the biology of chromosomal instability and leveraging these insights to drive forward a preclinical pipeline of therapies against innovative targets. Volastra is backed by top US and European venture firms such as Arch, Polaris, Vida, Droia, Catalio, and B Capital, alongside strategic investment from Eli Lilly. In addition to venture funding, Volastra has established partnerships with oncology leaders. Our senior leadership team is highly experienced, supported by a world-class advisory board. We are headquartered in West Harlem, New York City, within easy reach of Columbia, Cornell, Memorial Sloan Kettering, and surrounding areas. Please visit www.volastratx.com for more information. We are seeking an outstanding bioinformatics and computational biology scientist to help power Volastra's discovery and translational engine. This person will build and apply rigorous analyses of genomic, transcriptomic, functional genomic, and clinical-translational datasets to identify vulnerabilities, prioritize targets, nominate biomarkers, and connect preclinical findings to patient populations. This is a hands-on, high-impact role for someone who loves biology, writes excellent code, and can move fluently between exploratory discovery questions and decision-grade analyses. You will partner closely with discovery biologists, translational scientists, chemists, pharmacology colleagues, and the clinical team to generate insight that changes programs. We want a scientist who is excited to use modern AI coding assistants thoughtfully. AI should accelerate pipeline scaffolding, refactoring, testing, documentation, and exploratory coding, while human judgment, validation, data security, and scientific accountability remain an utmost priority. Volastra is a fast-paced biotech company of passionate employees, and there is ample opportunity for the ideal candidate to grow and develop with the organization. Individuals will have, from time to time, the opportunity to gain experience with projects outside their direct scope of work.

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.

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.