Senior Bioinformatician

Vivodyne creates human data before clinical trials, accelerating the successful discovery and development of human therapeutics. The Bioinformatician will develop new analytical methods and build robust infrastructure to support high-throughput -omics data generation, collaborating with cross-functional teams to drive biological insights and shape data strategy.

Responsibilities:

• Develop and Own Analytical Methods
• Design, implement, and critically evaluate computational approaches for scRNA-seq, bulk RNA-seq, secretomics, and proteomics data including QC, normalization, batch correction, dimensionality reduction, clustering, differential expression, trajectory analysis, and multi-dataset integration
• Go beyond applying standard tools: develop or adapt methods for perturbation modeling, causal inference, network reconstruction, ligand-receptor inference, and multi-omics integration as the science demands
• Select and standardize tooling based on rigorous benchmarking, not convention. Challenge default choices when the data or use-case warrants it
• Build Scalable, Production-Grade Pipelines
• Architect end-to-end analysis workflows in Python and/or R using modern workflow orchestration (Nextflow, Snakemake, or equivalent) that are reproducible, version-controlled, and designed to run at the scale of tens to hundreds of thousands of samples
• Partner with software and data engineers to productionize pipelines, ensure robust data ingestion and storage, and contribute to decisions on biological data architecture (storage formats, schema design, long-term data usability)
• Write well-documented, testable code that other engineers and scientists can run, review, and extend
• Drive Biological Insight and Interpretation
• Own dataset analysis to answer biological and client-driven questions with rigor. Ground conclusions in the limits of the data and clearly communicate what can and cannot be concluded with confidence
• Partner with experimental biologists to frame the most relevant biological questions, design analyses, and recommend experiments and data-generation strategies to fill key gaps
• Produce publication-quality figures, internal reports, and partner-facing deliverables that are decision-useful
• Shape Data Strategy
• Help define benchmarking strategies for internal tissue model development
• Proactively identify opportunities to leverage internal and public datasets to accelerate scientific progress
• Contribute to decisions around data standards, comparability, and the integration of omics features into downstream ML models and AI workflows
• Communicate Across Functions
• Translate complex multi-omic analyses into clear insights for biologists, AI/ML researchers, software engineers, and non-technical stakeholders
• Tailor depth and framing to the audience while maintaining scientific rigor