Aditya's Profound AI Rep
Aditya's view on following data shows how he handles findings that contradict his starting hypothesis.
Aditya Laddha
Edges
Running Primary Research End to End
Aditya does not treat primary research as a data collection task. He treats it as a design problem: what structure will surface the signal the brief actually needs? At FSG, he built a scoring framework from scratch to handle a situation where stakeholders held conflicting views and no existing format could reconcile them. At IQVIA, he designed blinded interview protocols for healthcare professionals to eliminate bias before a single question was asked. The pattern across both is the same: he structures the research approach before executing it, and he follows the data even when it surfaces something the original plan did not anticipate. For a team that needs someone who can pick up a vague brief and return structured, defensible findings, this is the muscle that matters.
Spotted in 2 Stories
F
Synthesizing Qualitative Research into Recommendations
Aditya's output is not a data dump. He takes qualitative inputs, whether expert interviews or stakeholder scoring, and converts them into structured findings that support a clear recommendation. At IQVIA, he combined clinical expert testimony with proprietary market data to produce a scenario-based view of a drug launch opportunity. At FSG, he converted scored stakeholder responses into quantitative analysis that surfaced a conflict the team had not anticipated. The consistent pattern is moving from messy, multi-source qualitative input to something a decision-maker can act on. He does not force the data to fit a prior; he follows it and adjusts the recommendation accordingly. For teams that need research to land as a usable output rather than a pile of findings, this is where he adds value.
Spotted in 2 Stories
FTracking Markets and Updating Views
Aditya keeps up with sectors he is not actively covering through podcasts, newsletters, and selective deep dives. When a signal contradicts his prior, he investigates rather than moves on. The FirstClub example is the clearest illustration: he started skeptical, saw a funding signal that did not fit his model, and researched until he understood why the premium quick commerce thesis was working in Indian metros. He updated his view based on what he found. For a research role that requires staying current across multiple sectors simultaneously, this is the habit that makes broad coverage sustainable.
Spotted in 1 Story
Aditya Laddha
About Me
Aditya Laddha is a research analyst with a background in management consulting and social impact advisory. An IIT Bombay graduate, he has built his early career running primary and secondary research across healthcare, development finance, and consumer markets.
He is currently an Associate at IQVIA, where he designs and conducts expert interviews and market analyses for pharmaceutical and healthcare clients, translating findings into structured recommendations.
At FSG, he owned the stakeholder research workstream for a South Africa-based development finance project, building a scoring framework to surface and reconcile conflicting stakeholder views across community, local, and national government levels.
Before that, he interned at Nomoex Global in a founder's office capacity, gaining early exposure to startup operations.
Aditya Laddha
When the data contradicts your hypothesis, that's not a problem. That's the research working.”
Aditya Laddha
On following the data