I help research teams turn biological data into decisions they can act on.

Sole life-sciences domain SME alongside a 15-person engineering team at a pharma client, translating stakeholder needs into user stories and technical requirements. The platform handles two linked problems: how scanner and collaborator data lands in the client's cloud and reaches the ELN and image viewer, and how the raw specimen manifest, grossing decisions and assay outputs stay tied to chain of custody.

When a pharma client moved genomics and transcriptomics to the cloud, the harder question was not whether the pipelines ran, it was whether the next engineer could pick them up and rerun them. My work sat at the intersection of validation, error triage, and developer documentation.

An early-stage biotech needed a bioinformatics function that could feed the wet lab, from pipelines and target shortlists to the siRNAs and primers that would actually test the predictions. Building it meant deciding what to run in-house, what to reuse from open source, and what a remote team of three could realistically ship.

Digital maturity is easy to talk about and hard to measure objectively. The framework we built made the conversation with 16+ stakeholders comparable across sites, and the scoring made the picture actionable rather than anecdotal.