Turning ambiguity into something testable
Most product problems start vague. I like turning unclear goals into something concrete enough to measure, test, and improve.
Shreenath Bhanderi
Building systems for growth.
I'm a Senior PM at Grow Therapy, a mental health marketplace, where I focus on the consumer marketplace experience: search, provider profiles, booking, and ranking. I love helping users make high-stakes, complex decisions easier, first in insurance at Young Alfred, and now in mental healthcare. My career has centered on one kind of problem: helping people make hard decisions inside systems they do not understand.
My strength is taking messy product systems, breaking them down with data, and finding the few levers that actually move behavior. Recently, I helped lead Marketplace Experience through a reset and delivered intake conversion wins across homepage, search, ranking, profile, and booking improvements.
What I Focus On
Clearer product work, better loops.
Marketplace and growth systems
I have spent most of my time working on acquisition, matching, scheduling, and retention, building systems for fast optimization.
Experimentation and iteration
I prefer work that can be inspected: clear hypotheses, measurable outcomes, and the discipline to separate signal from noise.
AI as a work surface
I am currently thinking about how people actually use AI for real work: research, writing, spreadsheets, recurring tasks, and the products that make that easier over time.
What I'm Exploring
Workspaces instead of chat
Chat resets too often. The more interesting direction is a system that remembers context, files, and prior work.
Delegation, not prompting
Most users do not want to write prompts. They want to hand off work and get something usable back.
Artifacts over answers
The useful output is rarely just text. It is a cleaned spreadsheet, a reusable template, a dashboard, or a plan.
Trust and verification
Knowledge work usually does not have clear tests. Products need better ways to inspect and verify outputs.
Writing
A few notes and essays on product thinking, marketplaces, and AI systems.
Living contextA note on why AI work needs a homebase where context can accumulate.Why chat starts to break downThe problem with rebuilding context every time a task gets more serious.What makes AI outputs trustworthyWhy source views, previews, diffs, and reversible changes matter.How product systems make decisions visibleNotes on turning messy product work into something teams can inspect.
About Shree
I studied Computer Science and Philosophy at Tufts, which still shapes how I approach product work: define the system, understand the constraints, and make decisions inspectable.
Most of my work has been in environments where the problem is not clean, and the goal is to make it clearer over time.