Esha Sharma.
AI Product Manager
I build AI product thinking — from teardowns and PRDs to research and metrics — focused on real user problems, measurable outcomes, and decisions that hold up.
Available for work.
About me.
I'm Esha Sharma, an AI Product Manager based in India. My background is in product analytics and data, SQL, Python, A/B testing, cohort modeling. Over the past year I've shifted entirely toward AI product work, building independently before being hired. I write PRDs, run product teardowns, and design metrics frameworks for AI products. I'm looking for AI PM, APM, and product strategy roles where analytical depth and product thinking both matter.
AI PM Projects
Analytics Projects
AI Products Tested
Services.
1.
AI Product Management
Product requirements and PRD writing
User research and problem validation
RICE prioritization and feature scoping
AI-specific considerations: privacy, failure handling, confidence
2.
Product Analysis & Teardowns
Identifying systemic product failures
Connecting user problems to business impact
Competitive benchmarking across AI products
Feature recommendations with outcome metrics
3.
Data & Analytics
SQL funnel analysis and cohort modeling
A/B experiment design and significance testing
Retention and churn analysis
Dashboard design and KPI frameworks
Stack.
Notion
PRD and documentation
PostgreSQL
Data analysis
Python
Analytics and experimentation
Tableau
Visualization and dashboards
ChatGPT
AI product research
Claude
AI product research
Experience.
AI Product Manager
Independent
Jan 2026 - Present
• Conducted a comparative AI trust study testing ChatGPT, Claude, and Gemini across 5 real task types, defined a 5-type failure taxonomy with real evidence and delivered a VP-level executive memo recommending confidence signaling as the highest-ROI retention lever.
• Designed a full concept PRD for Notion AI's missing Workspace Memory Layer, including user research, RICE prioritization across 3 competing features, AI privacy framework, and a 6-week MVP rollout plan with a built-in kill condition.
• Ran an independent product teardown of Perplexity AI using Reddit user research, identified 3 systemic problems tied to retention and trust, designed 3 prioritized feature recommendations each with a target metric, and defined a Northstar metric framework.
• Built a 5-metric AI trust measurement framework including Silent Failure Rate and Trust Recovery Rate (target 70%+), designed to capture not just accuracy failures but the ones users never notice, which are the most dangerous to product health.
• Delivered a 3-stage Trust Recovery Framework mapping detection signals, immediate response design, and long-term trust rebuilding strategies, structured for a product team to implement, not just observe.
What this work demonstrates.
1. Analytical depth
All projects Quote
Every project starts with real data, user research, Reddit threads, actual product usage. Opinions come after evidence, not before.
2. Business impact, not just features
Perplexity Teardown · Notion PRD
Problems are always connected to a retention metric, a conversion rate, or a trust signal. Features that don't move a number don't ship first.
4. PM-quality output, independently
