Sujay Jadhav Learned to Read the Signal Before He Learned to Trust the Noise

At 23, Sujay Jadhav found himself running a paging company in Cambodia, protected around the clock by bodyguards and driven in a bulletproof car. This was not the usual first rung on an engineer's ladder. The country was politically and economically unstable. Competitors were already moving. Jadhav, newly out of university and working for Singapore Telecom, had been elevated from project engineer to chief executive of a new subsidiary. His team had infrastructure to build, a market to enter and little reason to expect patience from either.

They launched 12 weeks ahead of the competition. Then they produced the first pager capable of displaying Cambodian characters. Jadhav says the business took 90 percent of the market in its first year. The details belong to another technological age, when a pocket-sized device buzzing with a line of text could feel like the future. The operating lesson has aged rather better: when the environment is chaotic, speed matters, but usefulness matters more.

Three decades later, the objects on Jadhav's desk are different. The signal is now scattered across electronic records, physicians' notes, images and claims. The noise is clinical shorthand, incompatible systems and missing context. As chief executive of Verana Health, he is trying to make that unruly material useful for research and clinical decisions. Cambodia was an early, hazardous tutorial in finding the message. Healthcare data is the graduate seminar.

The comparison should not be pushed too neatly. A pager either receives a message or it does not; clinical information arrives with ambiguities that resist such tidy engineering. Still, both jobs begin with infrastructure that most people never see. Towers, protocols and character sets once sat behind the beep. Today, ingestion pipelines, validation rules and specialist review sit behind a dataset. Jadhav has built a career in these backstage systems, the places where an elegant promise either becomes dependable or falls apart.

A family business, with an Apple IIe in the back room

Jadhav grew up in Australia in a family of physicians. As a teenager, he earned minimum wage helping in his father's medical office, handling bookkeeping and patient communications. He also spent weekends tinkering with ways to automate the practice. Medicine supplied the setting; the Apple IIe supplied the fascination. He created and played games on the machine and chose electronic engineering, cheerfully casting himself as the family rebel.

He also loved Australian-rules football, though he concluded that enthusiasm was doing more work than talent. Computers offered a more promising contest. They followed rules, but invited invention. An engineering degree from the University of South Australia and, later, an MBA from Harvard gave him two complementary languages: how systems work and how organizations decide what to do with them.

“In life, as in business, focused resilience is not just a trait, it's a strategy.”Sujay Jadhav

“Focused resilience” is a polished phrase, but Jadhav's career gives it rough edges. After telecom came consulting, then a long education inside Model N, the life-sciences software company. He has called his time alongside founder Zack Rinat a 14-year “second MBA.” The curriculum included pivots, near-death moments, expansion, contraction and the company's 2013 public offering. The enduring lessons, as he tells them, were almost severe in their practicality: spend carefully until product-market fit, pursue customer satisfaction without sentimentality and understand the timing of a market.

The startup that needed a smaller idea

When Jadhav became CEO of goBalto in 2013, he inherited an early-stage company whose ambition had outrun its definition. His response was subtraction. Clinical trials contain a long prelude of site selection, documents, approvals and coordination before a participant can be enrolled. Jadhav focused the company on that neglected study-startup phase. Internally, he framed the product as the “TurboTax of clinical trials,” a slightly comic comparison that did serious strategic work. Everyone understands the promise of making an intimidating process legible.

The personnel choice was just as revealing. Rather than usher the founder out, Jadhav identified the founder's gift for selling and moved him to lead sales. Collaboration, in this account, was not a poster on a wall. It was the work of matching a person's strongest instinct to the job that needed it.

By 2017, goBalto said its software was used by 18 of the 25 largest pharmaceutical companies and four of the five largest contract research organizations. Oracle acquired the company in 2018. Jadhav moved inside the buyer and ran product and engineering teams in its Health Sciences unit. If Cambodia taught speed and Model N taught market timing, goBalto taught the discipline of the aperture: a company can see farther after deciding precisely where to look.

There was another lesson in the acquisition. A startup can succeed by solving a narrow problem so thoroughly that it becomes a missing piece in a much larger system. Oracle already operated across the clinical-trial process. goBalto brought the opening act: planning, selecting and activating sites. For Jadhav, the move from a focused independent company into a broad platform was not a departure from the thesis. It was proof of it. The small aperture had revealed where the larger machinery needed work.

Turning the exhaust of care into something research can use

Jadhav joined Verana Health as CEO in August 2021. The company had been built around a rich but difficult asset: information generated during routine care, initially anchored in specialty registries in ophthalmology, urology and neurology. Such records are plentiful. They are not automatically coherent. A note written for the next clinician is not the same thing as a tidy row prepared for a researcher. A scan, a billing code and an observation in prose may each describe part of the same event without agreeing on the vocabulary.

Verana's proposition is that careful curation can turn those fragments into real-world evidence. Its systems ingest and normalize data, while machine learning and natural-language processing help locate detail buried in unstructured notes and images. The output can support trial design, find potential research sites, illuminate treatment patterns and help specialists measure the quality of their work.

Jadhav is bullish about artificial intelligence, but his version comes with a conspicuous brake pedal. Onstage in 2022, he noted that machine learning was itself still learning. Bias had to be found and corrected. More recently, he has argued that AI scales clinician expertise rather than replacing it. Models require specialists to help design them, validate iterations and check the final cohorts. In an industry that can make a parlour trick out of certainty, the caveat is the interesting part.

This also explains his fondness for collaboration. Verana's work depends on relationships with medical societies, practitioners, technologists and life-sciences teams, groups that do not always begin with the same incentives or vocabulary. The model is only one participant in the room. Jadhav's job is partly architectural: arrange the experts so that each can challenge the assumptions of the others, then turn that friction into a product sturdy enough for consequential use.

His questions for evaluating an AI platform are remarkably unromantic. Does it perform? Can it ingest different kinds of data? Has someone checked the model for bias? Does the company understand the relevant specialty? These are not the questions of a futurist peering at the horizon. They belong to the engineer in the machine room, tapping the gauges.

“AI is a tool that just scales the clinician expertise; it's definitely not a replacement.”Sujay Jadhav

The merger that turns a sequence into a network

In January 2026, Verana merged with COTA, a company specializing in oncology real-world data. The combination added cancer expertise to Verana's established work in eye, neurological and urological care. The joint network represents 95 million de-identified patients and 25,000 providers. Scale is an obvious part of the pitch. Depth is the more consequential word. A large pile of ambiguous records remains a large pile of ambiguous records.

By September, the broader strategy had acquired a conversational interface. Verana introduced Agent Claire for Life Sciences, disease-specific AI agents designed to let research teams ask questions of its datasets without first writing code. The answers include the underlying query logic, ready for a data scientist to validate. It is a revealing compromise between access and accountability: make the front door easier to open, but leave the workings visible.

Jadhav describes the moment as an inflection point: real-world evidence moving from a retrospective tool toward something that can inform trial strategy earlier. The ambition is to help determine whether a study is feasible, where participants might be found and how results compare with well-matched historical experience. In some carefully designed studies, an external control arm built from existing data can supplement a conventional control group. Jadhav is careful to say such methods do not simply abolish randomized trials. Quality, protocol design, documentation and regulatory alignment still decide whether the evidence deserves belief.

There is a pleasing continuity here. The young telecom engineer built infrastructure so a message could arrive. The enterprise-software executive made complicated commercial processes tractable. The startup CEO attacked the paperwork before a trial began. Now the health-data CEO is asking whether the record left behind by ordinary care can arrive, cleaned and contextualized, at a decision that matters.

The technology grows more sophisticated at every stop. The management instinct stays almost stubbornly plain: choose the problem; narrow the field; invite the people who know it; move; check the result. Focus without resilience becomes brittle. Resilience without focus is merely a long walk in fog. Jadhav's phrase requires both.

Cambodia's pager market has vanished into technological prehistory. The local characters on those tiny screens remain a useful emblem. A system becomes valuable only when it speaks the language of the people who need it. For Verana, that means researchers, life-sciences teams and clinicians. For Jadhav, it means listening through the static, then earning the right to call what remains a signal.