NonclinicalVial · drug development
Solving preclinical data organization
CDMO spreadsheets in; QC-flagged data and one-click IC50 curves out.
→Read case studyVP of Product · Vial · clinician-builder
Founding product hire at Vial, APM to VP in four years. Built the eClinical platform from zero — EDC, RTSM, portals — for patients, sites and monitors, then the AI tooling that runs on top of it. Before that: oncology research coordinator on CAR-T and immunotherapy trials at UCSF and MGH.
Currently · VP Product at Vial
🩺 bedside → build
APM → VPin four years, as the founding product hire
5 appsshipped 0→1: EDC, RTSM, Start-Up, Monitoring, Admin
60 → 92%first-pass catch rate for the AI clinical monitor
20+AI skills and pipelines in daily production use
What kind of PM am I?
The kind who asks the uncomfortable question first — usually of myself.
01
It's how you communicate 90% of the time that matters.
The 4pm Slack reply, the ticket, the tradeoff explained to a coordinator — that is what people experience of a PM. I write clearly, early, and to whoever has to act.
02
Does everything I do meaningfully support the business?
The question I ask most, and the easiest to avoid. If the honest answer is a shrug, I stop, say so, and redirect the effort.
03
First principles, so that answer stays honest.
Reason from what the trial, the site or the company actually needs — not from the roadmap, and not from what a vendor happens to do.
The story · bench → bedside → build
One path through the pipeline, told in the products.
OriginUCSF · MGH
It starts at the bedside.
Oncology research coordinator on CAR-T and immunotherapy trials. Every product decision since has been a vote for the person entering the data — and for changing how core healthcare is delivered, industry-wide. It is the work I have given my life to.
CAR-T & immunotherapy trialsHeme-Onc
01
Chapter 01
Nonclinical — the bench
Before a drug reaches a patient it generates thousands of assay rows nobody has time to clean.
02
Chapter 02
Clinical — the trial
The part of the pipeline where the software is regulated, the people are tired, and every field has a reason.
03
Chapter 03
AI tooling — the layer
AI that touches trial data has to earn it. Mine keeps a human in the loop until it does.
OriginUCSF · MGH
It starts at the bedside.
Oncology research coordinator on CAR-T and immunotherapy trials: consenting patients, chasing queries at 7pm on Fridays, learning exactly what a badly designed eCRF costs a site. Every product decision since has been a vote for the person entering the data. Underneath every title since is the same commitment: changing how core healthcare is delivered, industry-wide. It is the work I have given my life to, and the work I intend to keep giving it to.
CAR-T & immunotherapy trialsHeme-OncJiang 2016
01
Chapter 01
Nonclinical — the bench
Before a drug reaches a patient it generates thousands of assay rows nobody has time to clean.
02
Chapter 02
Clinical — the trial
The part of the pipeline where the software is regulated, the people are tired, and every field has a reason.
ClinicalVial · 2022 →
Building our own clinical trial software
A Part 11 EDC, RTSM and portals, built in-house and running five trials.
→Read case studyClinicalVial · data management
Automating clinical data management
Coding, queries and TMF tracking done by software; a person signs every run.
→Read case studyClinicalVial · program management
Shipping regulated software every two weeks
A validation package that is ready the day the code merges.
→Read case study03
Chapter 03
AI tooling — the layer
AI that touches trial data has to earn it. Mine keeps a human in the loop until it does.
AI toolingVial · AI product
Using AI to review trial data
An agent drafts the findings, a monitor approves them. Catch rate 60% → 92%.
→Read case studyAI toolingVial · AI product
Drafting regulatory documents with AI
IBs, protocols and Pre-IND packages assembled from program data, sources cited.
→Read case studyHow I work
Forward-deployed, by default.
I don't hand problems to a backlog. I sit with the coordinators and monitors, then build — and most of what I build runs in production the same month.
01
Forward-deployed.
I sit in the standup, read the queries, and ship the fix that week. The roadmap is written in the site's problems, not in a slide.
02
Human-in-the-loop until earned.
Regulated AI starts assistive. Override rate is a feature, and autonomy is something a system graduates into with evidence.
03
Spec it so an agent can build it.
A ticket precise enough for an AI agent to implement is a ticket precise enough for anyone. One engineer reviews; the team's hours go to judgement.
04
Validation is product.
The release isn't done until the package is signature-ready. Documentation that lags the code is a bug.
build log · things I shipped, not slides6 tools
clinical-monitor
AI remote data review over EDC data
medical-coding-automation
Bi-weekly MedDRA / WHODrug pipeline
ib-generator
22-prompt Investigator's Brochure drafting
pind-generation
16-section Pre-IND meeting packages
pbmc-assay-processing
CDMO assay files → QC-flagged, Scispot-ready
deployment-docs
FRS, trace matrix and OQ updated per release
Open-sourcing the non-proprietary pieces. Repos landing here as they're cleaned up.GitHub · coming soon
Hands on, myself:SQL — I pull my own data (Metabase, Postgres)Python data pipelinesReact prototypesn8n automationsClaude skills & agentsFigma → FramerLinear, written like a spec
A Tuesday, with AI.
How it actually shows up in a day — not a strategy, a habit.
A skill has already posted the daily market briefing to Notion. I read it; I didn't write it.
Standup. Two tickets specified tightly enough that a coding agent implements them; one engineer reviews by lunch.
A question comes up about enrolment. I write the SQL myself in Metabase and answer it in the thread, with the chart.
The clinical monitor has drafted this week's findings from the EDC. I review the overrides, not the records.
Prototype a new payment-milestone flow in an afternoon with Claude. The screenshot goes to ops and design to decide, not to debate.
Draft the weekly readout from Linear and the deployment log with a skill. I edit the judgement, then ship it.
AI drafts. I decide. Anything regulated keeps a human signature.
Off the clock
Personal projects, mostly handmade.
Side venture
Sour Cherry
A gifting brand for Indian weddings and festivals — welcome bags first, Holi and Diwali next.
Textiles
Brishti
Silk headscarves. Monsoon-named.
Poetry
Sunday Morning Tabla
A collection, written in the long shadow of Tagore and Rumi.
Suno · Ableton
Generative music
Experiments in letting a model hum and a human arrange.
Let's build something regulated and delightful.
Open to Director / Head / VP of Product roles in clinical-trial tech, eClinical, digital health and life-sciences tooling. Remote US, NYC or the Bay Area.
© 2026 Dhruba Biswas · built by hand.Bench → bedside → build.