Athul's Profound AI Rep
Athul's take on data-driven decisions shows how he separates statistical significance from business significance.
Athul P M
Edges
Owning Research Workstreams End-to-End
Athul takes ambiguous operational briefs and drives them through to live, measured outcomes without a senior researcher above him. He scopes the problem, designs the approach, runs the analysis, and delivers the final output. This pattern is visible across the seat-change optimizer at a top US airline and the spend taxonomy project for a $10B+ procurement portfolio. In both cases, he owned the full arc from problem definition to stakeholder sign-off. What makes this repeatable is his instinct to start with the research before committing to a solution. He ran boarding pattern analysis and passenger behavior research before designing the optimizer, and spent months with category managers before proposing a taxonomy structure. He also closes the loop with measurement: the A/B test across 4,200+ flights and the real-time Power BI dashboard were both his design, ensuring the work was verifiable, not just delivered.
Spotted in 2 Stories
Separating Signal from Noise in Complex Data
Athul has a consistent pattern of working through large, messy datasets and making defensible calls about what actually matters. He does not treat statistical significance and business significance as the same thing, and he is willing to push back when they diverge. On the supply chain taxonomy, he challenged legacy categories that had millions of invoices but minimal dollar impact, grounding every cut in a business logic he had validated with the Director of Procurement. On the seat-change optimizer, he designed an A/B test that normalized across four dimensions to ensure the two flight populations were genuinely comparable before drawing conclusions. This instinct comes from his engineering background combined with months of working directly with domain experts. He learns the business context first, then applies the analytical framework. The result is analysis that stakeholders trust, because the calls are traceable to both the data and the business rationale behind them.
Spotted in 2 Stories
Translating Technical Complexity for Stakeholders
Athul bridges the gap between technical solutions and the people who need to approve and use them. He does not just present findings; he translates the underlying logic into terms that allow non-technical stakeholders to make informed decisions. On the seat-change optimizer, he spent significant time explaining to operational heads why a near-optimal result was the right outcome, and why a perfect result was not achievable. He reframed the mathematics as a business trade-off, which was what the stakeholders actually needed to hear. On the supply chain taxonomy, he aligned the Director of Procurement on a framework for what made a category worth keeping, turning a data-driven recommendation into a shared decision. His engineering background gives him the technical depth to understand what is actually happening inside a model, and his consulting experience has trained him to communicate it without losing the audience.
Spotted in 2 Stories
Athul P M
About Me
Athul is an analytical consultant and research-led operator with two years of experience building data products and decision tools for large-scale operations. He holds a degree from IIT Kharagpur and has worked across operations, supply chain, and workforce analytics at Indus Insights.
He currently leads research and analytics workstreams at Indus Insights, owning problem scoping, solution design, and stakeholder delivery end-to-end across a top-three US airline client.
His most significant project was a seat-change optimizer for a major US airline, which he took from a vague operational brief to a live A/B test across 4,200+ flights, delivering $6.6M in annual staffing savings.
He also defined a four-tier spend taxonomy for a $10B+ procurement portfolio, requiring him to push back on legacy classifications and align senior procurement leadership on what actually mattered at scale.
Earlier, he conducted primary research on payment platform adoption in small restaurants at IIM Kozhikode, combining field surveys with machine learning to draw market insights.
Athul P M
Statistical significance and business significance are not the same thing. I've learned to treat them separately, and that's usually where the real call lives.”
Athul P M
On data-driven decisions