Hire Data Analyst for Biotech Startups
Senior, direct, no layers

The scarcest resource at a biotech startup is focused scientific time. Every hour a scientist spends reformatting data, chasing a CRO, or rebuilding an investor update is an hour not spent on the science the company exists to do.

Good analysis here is not a dashboard nobody opens. It is a small number of trustworthy views, each tied to a decision, with the definitions written down so two people reading the same number reach the same conclusion.

Picture a CRO dataset arriving in a format nobody can use, days before an investor update is due. In that situation, an analysis showing exactly where cases stall in CRO study management — with the numbers behind each claim — would turn a debate about workload into a decision about where to fix it. Book a free discovery call and we will test the fit on one real workflow before any wider scope is agreed.

an electronic lab notebook such as Benchling, a LIMS, bioinformatics pipelines, and CRO data portals already cover much of the work at biotech startups. What is usually missing is the routine around them: consistent handoffs, visible status, and checks that catch errors before they reach research partners, investors, and CRO teams.

The analysis reads from an electronic lab notebook such as Benchling and a LIMS and writes back views the team already uses, whether that is a dashboard, a report, or a spreadsheet. No new system of record is created.

This page describes services, not scientific or regulatory advice. Research and regulatory decisions stay with the startup's qualified scientific staff. FDA IND and BLA pathways shapes what can be automated or changed, and the work is scoped to fit it rather than work around it.

For a startup like this, success looks like scientists spending time on science, partners getting clean data, and milestones documented well enough to survive diligence.

Two problems deserve naming: experimental data scattered across notebooks and drives and CRO results arriving late or in unusable formats. Both persist when the underlying routine stays manual and nobody owns the follow-up, so each deliverable in this engagement is paired with a named owner and a visible queue, and problems surface while they are still small.

What changes is the meeting: decisions start from agreed numbers, bottlenecks are named with evidence, and arguments about the data itself fade away.

Browse all hire engagements or discuss your biotech startup workflow directly.