Sparsh's Profound AI Rep
Sparsh builds research from the hypothesis down and communicates findings that hold up under scrutiny.
Sparsh Goel
Edges
Hypothesis-Driven Research Design
Sparsh builds research from the hypothesis down. Before a question is written or a call is scheduled, he has already mapped what he needs to prove and why each data point matters. This pattern showed up at Dalberg, where he grounded every questionnaire segment in secondary research signals before fielding a single question. It showed up again at McKinsey, where he built a structured scenario map before conducting expert interviews on cross-sell opportunity. The mechanism is consistent: break the problem into specific hypotheses, design the research to test each one, and let the evidence determine the conclusion rather than the brief. The result is research that holds up under scrutiny, because the structure was built to surface what is true, not to confirm what the client hoped to find.
Spotted in 3 Stories
Translating Evidence into Decisions
Sparsh turns research findings into structured, actionable conclusions, even when those conclusions contradict the original thesis. He separates what the data supports from what it does not, and communicates that distinction clearly to senior audiences. At McKinsey, he found that the fiduciary tailwind thesis was stalled and communicated that directly to a PE client who had expected a different answer. He held the position under pushback, supported by expert testimony and a clear confidence assessment. At Dalberg, he converted raw survey data into a benchmarked investment opportunity map, layering in qualitative findings to explain the numbers rather than just report them. The pattern is consistent: the conclusion leads, the evidence follows, and the limitations are stated explicitly rather than buried.
Spotted in 3 Stories
Testing Sector Theses Against Constraints
Sparsh does not accept sector narratives at face value. When a thesis looks compelling on the surface, he traces it upstream to find where the structural constraints sit. On the ethanol blending opportunity in India, he moved past the renewable energy framing to examine the water intensity of the feedstock crops, identifying a supply-side constraint that the optimistic forecasts were underweighting. On the fiduciary tailwind thesis in US retirement services, he found that the regulatory landscape had been stalled for years and was not a reliable business lever. The pattern is consistent: read the optimistic case, then find the constraint that the headline does not price in. This is the instinct that makes research useful in an investment context, where the cost of a wrong thesis is real.
Spotted in 2 Stories