Ron Yadin

I began at Berkeley on the pre-med track, with coursework in molecular physiology, immunology, and endocrinology. Undergraduate research — on proteins bacteria use against each other, and on enzymes for biofuel production — then drew me toward applied biotechnology. Throughout, I kept programming independently.

After several years in industry, I joined the inaugural cohort of Berkeley's Molecular Science & Software Engineering master's program to formalize my software training: software engineering in Python and C++, data science, and machine learning. I completed the first year with a 4.0; when the curriculum shifted toward quantum chemistry, I withdrew to focus on my full-time startup role, and made the move into the dry lab, the goal from the start.

Amyris showed me what's possible: robotic workcells running DNA assembly, sequencing, and verification around the clock, on mature, enterprise-grade data infrastructure. It also showed me how few people could build that infrastructure.

The startups showed me the other end of the spectrum: small teams building their tools and processes from scratch. At Sestina Bio I began writing Python and Benchling automation to multiply our team's design work. At Shiru I turned unstructured, low-throughput experiments into an automated screening platform, and helped build the lab itself — from a shared incubator bench to a purpose-built headquarters with liquid-handling automation, pilot-scale fermentation, and a test kitchen.

Seeing both ends taught me that the right digital strategy depends on a company's stage and priorities, and that the people who can bridge science and software are what most labs are missing.

At Shiru, shortening the path from science to insight became my job. I designed a unified Benchling data model connecting a seven-stage research pipeline, from our high-throughput biology lab through fermentation to our test kitchen, while learning engineering fundamentals from a CTO, Head of ML, and senior data scientist who invested in me.

At 21st.BIO, I built the clean data model for multi-round genome engineering, the initial cloud environment, and the integrations that put structured data in front of scientists. I drove the company's R&D data strategy, directly managed an associate lab data engineer, technically supervised an automation engineer, and ran a successful summer internship program.

Running the evaluation that brought new scientific software into the company taught me the hardest part of digitalization usually isn't the technology. It's making the case that earns an organization's commitment.

Making the case for digitalization is now my full-time job: I help R&D, IT, and executive leaders connect what their scientists need to their most critical business outcomes, then prove it with proof-of-value work on their hardest problems.

Years in industry, and more than a decade of practice explaining complex science clearly, have taken me to the stage three times — at Nordic Digital Science & Innovation Day 2025, the Swiss Digital Science & Innovation Forum 2026, and Benchtalk Boston 2026 — to share my vision for the future of digitalization, structured data capture, and AI across biotech R&D.