Apramay Gyan — Product Manager

Career

Work experience

Jan 2025 – Present

Jinee Inc

Philadelphia, PA

Product Manager

Grew monthly active users 45% by defining product instrumentation, SQL-based cohort models, and a personalization framework to surface high-intent users.
Improved onboarding conversion 30% by partnering with ML engineering to translate ranking logic into actionable user stories across a three-sided platform.
Shipped a GenAI-powered self-service solution co-architected from feature scoping to production — cutting manual processing 90% and lifting client autonomy 60%.
Led multi-variable A/B experimentation across billing, risk, and compliance, lifting 30-day engagement to 60%.

Jan – May 2024

Astral Insights

Philadelphia, PA

Consultant (Capstone)

Improved profitability forecast accuracy 20% by surveying 100+ maritime professionals and building KPI frameworks to model key revenue levers.
Identified 75 strategic market opportunities via competitive landscape analysis to refine Total Addressable Market.

Jul – Dec 2023

Park Genius

Philadelphia, PA

Product Manager (Intern)

Shaped core product roadmap through 50+ user interviews, directly driving GPS tracking and real-time availability features.
Validated MVP with entrepreneurs-in-residence via Figma prototype and API documentation, securing sign-off for launch planning.

Nov 2021 – Jul 2022

Infosys Limited

Bengaluru, India

Associate Consultant

Reduced system incidents 30% and delivered $2M in cost savings migrating 200 servers via Azure DevOps & CI/CD with zero post-migration defects.
Accelerated SLA adherence 40% through cross-functional collaboration for Reckitt's SAP cloud migration.

Jul 2019 – Nov 2021

TCS

Kolkata, India

System Engineer

Maintained 99.99% uptime across 160+ countries for KPMG managing identity and access management across hybrid cloud and on-prem environments.
Improved account-creation cycle time 80% by redesigning the end-to-end workflow with a 10-member global team.

Portfolio

Case studies & projects

Data Visualization · Public Portfolio

Tableau Analytics Dashboards

Interactive dashboards spanning product analytics, market analysis, and data storytelling — each reflecting the analytical rigor I bring to product decisions.

Open dashboards

GenAI · Platform

GenAI Self-Service Platform at Jinee

Manual processing was the core bottleneck on Jinee's three-sided compliance platform — clients had to rely on internal teams for every workflow step, creating delays and scaling problems. I identified this as both a retention risk and a product opportunity, then co-authored the full technical architecture with ML engineering to replace human-in-the-loop steps with a GenAI assistant.

90%

Processing time cut

60%

Client autonomy lift

0→1

Shipped to production

My role

Product lead — feature scoping, architecture review, user story definition, launch

Problem

Clients needed human support for every action — couldn't scale, hurt retention

Solution

GenAI-powered self-service layer embedded in the platform's core workflow

Stack involved

LLMs, ML engineering, compliance workflows, billing and risk integrations

Key outcome

Clients could resolve issues independently without support tickets — dramatically reducing operational load and increasing platform stickiness.

Growth · ML

Personalization Framework — 45% MAU Growth

The platform lacked a systematic way to identify and re-engage high-intent users. I built the product instrumentation layer from scratch, then designed SQL-based cohort models that segmented users by behavior signals — feeding directly into a personalization framework that surfaced the right content and prompts at the right moment.

45%

MAU growth

30%

Onboarding conversion

My role

Defined instrumentation, built cohort logic in SQL, led ML collaboration for ranking

Problem

No visibility into user intent — couldn't differentiate active from at-risk users

Solution

Cohort models + personalized surfaces showing high-intent users the most relevant actions

Tools used

SQL, Snowflake, ML feature engineering, product analytics

Key outcome

Monthly active users grew 45% and onboarding conversion improved 30% — driven by surfacing the right moments to the right users, not just more notifications.

Experimentation

Multi-Variable A/B Framework across Billing, Risk & Compliance

No formal experimentation culture existed. I designed and led a multi-variable A/B framework that ran tests across three high-stakes workflows simultaneously — billing, risk, and compliance — without them interfering with each other. The goal wasn't just to optimize individual steps, but to identify leading indicators that predicted long-term retention.

60%

30-day engagement

3

Workflows covered

My role

Designed framework, defined metrics, ran experiments, analyzed results, socialized findings

Problem

Decisions made on intuition — no data to validate what drove real long-term retention

Solution

Structured A/B framework with guardrail metrics and a leading indicator model

Tools used

SQL, product analytics, statistical significance testing, Snowflake

Key outcome

30-day engagement reached 60% — but more importantly, the team now had a repeatable framework and shared language for making product bets with confidence.

Personal Project

Pulse — AI Product Analytics Copilot

PMs drown in raw metric dumps and spend too much time manually synthesizing data into something stakeholders can act on. I built Pulse — a working AI-powered tool that takes raw product data (funnels, A/B results, churn reports, user feedback) and instantly surfaces risks, opportunities, and next actions in PM-grade format. Built entirely end-to-end as a live demo.

Live

Working demo

3

Analysis modes

AI

Claude-powered

Problem

PMs spend hours manually structuring raw metrics into insight summaries

Solution

Paste any product data → AI returns structured risks, opportunities, and recommendations

My role

Solo — ideated, scoped, designed UI, prompted the AI layer, shipped to demo

What it shows

GenAI product thinking, self-service tooling instinct, end-to-end ownership

Why it matters

This is the same GenAI self-service pattern I shipped at Jinee — applied to a problem I personally felt as a PM. Built in days, demoed in interviews, fully functional.

Capabilities