Bioinformatics Scientist – Epigenomics and Computational Methods

Zymo Research is seeking a Bioinformatics Scientist to join the Epigenetics Technology team and lead computational development for epigenomics and molecular assay research. The scientist will curate datasets, discover and evaluate candidate methylation biomarkers, develop statistical models, and build reproducible analysis pipelines in close collaboration with experimental scientists.

This role combines epigenomics, statistical modeling, and software development. The scientist will guide computational strategy, define analytical performance criteria, and support targeted assay development, independent validation, and transfer into scalable research products.

Join us in making a meaningful impact!

• Own the computational roadmap for assigned research and assay development projects, defining deliverables, milestones, acceptance criteria, data needs, and technical risks. Coordinate priorities and dependencies with experimental scientists and support bioinformatics teams.
• Curate public and internal epigenomic datasets (methylation, hydroxymethylation). Document sample provenance, quality, relevant metadata, assay platform, genome build, and usage permissions; maintain versioned datasets with clear inclusion and quality criteria.
• Discover and prioritize candidate molecular markers and differentially methylated regions. Evaluate specificity, biological variability, common sequence variants, repetitive regions, assay compatibility, and stability across relevant sample and biological conditions.
• Develop and benchmark statistical models for molecular profiling and epigenetic biomarker analysis. Evaluate feature selection, model assumptions, uncertainty and performance supported by the available data.
• Contribute to other projects requiring relevant scientific and technical expertise.

• Build reproducible pipelines for methylation sequencing and relevant reference array datasets, covering quality control, alignment, methylation quantification, filtering, annotation, and reporting. Evaluate conversion efficiency, mapping ambiguity, duplicates, coverage, and batch effects using controls appropriate to each assay.
• Co-design targeted methylation panels with the assay team. Perform in silico assessment of primers, probes, and hybridization capture baits for sequence specificity, common variants, conversion-related sequence changes, and cross-hybridization; use empirical enrichment and coverage data to refine panel design.
• Benchmark analytical methods using reference standards, simulations, and independent sample sets. Separate model development, marker selection, and validation by biological source; assess bias, error, agreement, reproducibility, and robustness.
• Assess robustness to sequencing depth, missing data, DNA input, dataset selection, platform differences, batch effects, and relevant biological variation. Use validated harmonization approaches and prevent information leakage across development and validation datasets.
• Define quantitative reporting metrics, calibration approaches, quality thresholds, and validated reporting ranges. Document uncertainty, investigate unexpected results, and communicate analytical limitations.
• Implement automated quality checks, failure flags, versioned reports, and traceable software releases. Use code review, tests, workflow automation, and documented environments to support reproducibility and technical transfer; communicate model limitations clearly to scientific and product teams.

• Provide technical guidance and code review for collaborating scientists and junior bioinformatics staff. Maintain clear analysis documentation, contribute to publications and invention…