Utsav's Profound AI Rep
Utsav's take on research and problem-solving shows how he separates the question from the answer.
Utsav Agarwal
Edges
Structuring Quantitative Research Problems
Utsav's consistent pattern is taking a vague or large-scope problem and narrowing it to a set of testable, data-backed levers before building anything. This showed up at Blockhouse, where he spent the first two weeks mapping the data structure and KPIs before touching analysis, and at Notion Demand, where he evaluated multiple industries before committing to retail and a specific public dataset. His method is to define the right question first: identify the drivers, select the data source, validate assumptions through hypothesis testing, then move to output. He applies this sequence whether the domain is bond execution, inventory forecasting, or labor market intelligence. The result is research that holds up under scrutiny because the scope decisions are made explicitly and documented.
Spotted in 2 Stories
D
Quantitative Analysis Across Asset Classes
Utsav has applied quantitative methods across OTC bonds, equity portfolio optimization, and ML-driven forecasting, building a cross-domain analytical toolkit. At Blockhouse, he analyzed post-trade execution data across Treasury instruments, optimizing for slippage and transition cost. At Indxx, he deployed CVaR and hierarchical risk parity models for portfolio construction. At Conscience AI, he worked on price forecasting and trend prediction using hybrid deep learning models. His mathematical foundation runs through all of it: back-testing, hypothesis testing, KPI selection, and model validation are consistent tools regardless of the asset class or domain. For a research role, this means he can pick up a new instrument or market structure and apply a rigorous analytical process without needing to rebuild his toolkit from scratch.
Spotted in 3 Stories
DI
Translating Research into Stakeholder Decisions
Utsav has consistently presented research findings to senior stakeholders — CEOs, co-founders, and HR heads — and structured his communication around what they need to decide, not just what the data shows. At Notion Demand, he briefed the CEO twice a week at each stage of the research sprint, presenting the reasoning behind scope decisions and the data validating each step. At Sartre Group, he prepared and presented labor law documentation to a Head of HR at a financial exchange, enabling a cross-border hiring decision. His approach is to lead with the validated finding and the decision it supports, then provide the supporting data. When findings do not support the expected narrative, he treats it as a scoping problem and brings the stakeholder into the revision process. This makes him useful in research roles where the output is a recommendation, not just a report.
Spotted in 2 Stories
DUtsav Agarwal
About Me
Utsav Agarwal is a quantitative researcher and analyst with a background spanning financial markets, machine learning, and labor market intelligence. He has worked across quant analysis, AI research, and research consulting at a series of early-stage startups and specialist firms.
He currently works as an Associate Researcher at Sartre Group, where he runs end-to-end hiring pipelines for finance industry clients, delivers market intelligence and labor law advisory, and manages stakeholder relationships from brief through to close.
At Blockhouse, he analyzed a dataset of roughly 58,000 OTC bond trades and identified $4.2M in transition cost savings by optimizing trade size, market timing, and dealer selection across a three-month engagement.
At Notion Demand, he owned a full research-to-model sprint on retail inventory optimization, from literature review and industry selection through EDA, hypothesis testing, and a working hybrid neural network forecasting model.
At Indxx, he worked on portfolio optimization using CVaR and hierarchical risk parity models, building his quantitative finance foundation.
Utsav Agarwal
If the data is not supporting the answer, the problem needs revisiting, not the data.”
Utsav Agarwal
On research and problem-solving