Product Manager · product discovery

Kunal Kumar

A product manager who takes products from a blank page to live users.

5Products live
0→1Marketplace shipped
4 yrsHealthcare & streaming
MScBayes, Distinction
01 — Products

Software that is live, right now.

Five products in production across healthcare, developer tooling and commerce — built end to end, from the first requirement doc to the release notes.

02 — Work

Problem. Decision. Outcome.

Select any role to expand it.

01 Google Product Strategist, Industry Project

YouTube Connected TV · Product discovery and go-to-market strategy · London, UK · South Africa market

Problem

Advertisers treated YouTube as mobile-first and lower-income, so premium above-the-line video budget kept going to linear TV.

Decision

Tested 3 planning assumptions — age, income and attention quality — against consumption evidence rather than arguing from platform data alone.

Outcome

Sized the living-room opportunity at a third of national YouTube watch time across 8.5 million smart-TV households. Recommendations adopted to rebalance video investment.

1/3of national watch time
8.5Msmart-TV households
+16%watch time YoY
+29%views YoY
02 Clickmate LifeCare Associate Product Manager

0 to 1 healthcare marketplace · Practitioner onboarding, hospital partnerships, emergency ambulance dispatch · London, UK

Problem

No product existed. Requirements arrived as complaints, and a healthcare marketplace with thin practitioner coverage cannot answer a request at all.

Decision

Built supply before demand — practitioner density and specialty spread had to lead. Specified request routing end to end: service-type matching, an escalation ladder for unacknowledged requests, on-call fallback, every failure path.

Outcome

Launched in 11 months as the only product person, with hospital partnerships for ambulance dispatch and a release process that almost never went backwards.

1,500+practitioners in 3 months
−18%support requests
95%rollback-free releases
03 Xperi Inc. Associate Product Manager

Android TV streaming platform · Product analytics · Agile / Scrum · Remote, US markets

Problem

Three regional launches on one codebase, and engineering debt slowing every release.

Decision

Proposed Multiview off behavioural analysis showing users switching between live events and losing the thread. Owned the backlog through an RxJava v1 to v3 migration, sequencing 50+ tickets across 15+ streaming modules to keep shipping while the rewrite landed.

Outcome

Incremental watch time and a rise in monthly active users across the Olympics, owning US streaming delivery. Validated in ReTA on playback behaviour, not install counts.

30,000+incremental watch minutes
50+migration tickets
3regional markets opened
04 Xperi Inc. Product Management Intern

Fire TV · Android TV · Mobile · REST APIs · Firebase · Remote, US markets

Problem

Integration defects surfaced late, during pre-release checks, on platforms serving millions.

Decision

Tracked build status across every environment and flagged issues before they reached the pipeline, while keeping acceptance criteria and API contract notes current.

Outcome

Three consecutive regional deployments on schedule, with the squad working from a single source of truth.

3on-schedule deployments
5sprints, one source of truth
2TV platforms
03 — Ventures & Innovation

GrocGuardian

0 to 1, from scratch. Jan – Mar 2026.

AI grocery intelligence for households that throw away £800 of food a year.

Built during MSc Management at Bayes Business School and qualified for VC funding at a Bayes entrepreneurship event. The wedge is zero manual entry — receipt AI reads the shop, computer vision tracks expiry, a recipe engine spends whatever is about to spoil.

  • Receipt AI
  • Expiry computer vision
  • Recipe engine
  • Zero manual entry
£800 wasted on food per UK household, every year
71% of surveyed households would actively use it
63% would pay £3–5 a month for it
Problem

UK households lose roughly £800 a year to food waste, 9.5 million tonnes nationally. The cause is invisibility rather than carelessness: no real-time view of what is in the house, expiry dates nobody is tracking, duplicate buying, and dinner decisions made without reference to what is already in the fridge.

Insight

Existing trackers already solve this on paper — and are abandoned within a week, because every one of them requires manual entry. The failure is not the tracking model, it is the input cost.

Validation

Ran primary research before building anything, March 2026. 75% cited over-buying as the top waste driver, 71% would actively use the app, 64% have food expire unnoticed, and 57% said lack of time makes it worse. 82% of respondents were 18–24.

Product

Designed the flow as three steps with zero manual entry: AI reads the shopping receipt and logs every item, computer vision reads expiry dates off packaging and schedules reminders, and the recipe engine works from live inventory, prioritising whatever expires first.

Model

Freemium — a free tier capped at three scans a month, premium at £3–5. The price came from the research rather than a guess: 63% were willing to pay £3–5 a month and 35% indicated £5+.

Market

Beachhead on university campuses for the lowest CAC and the highest word-of-mouth velocity — one flat is four users. Roughly £180M serviceable obtainable market inside a £4.5B serviceable market of tech-adopting households aged 18–45.

Roadmap

Three phases: validate an iOS and Android MVP in closed campus beta (months 0–6), freemium App Store launch with an ambassador programme and premium go-live (6–18), then supermarket API integrations, predictive replenishment and a carbon-footprint layer (18–36).

Moat

Software-only delivery of smart-fridge-level intelligence at zero hardware cost — the one position manual trackers, recipe apps and smart fridges each cannot hold at once.

04 — Teaching

Explaining it is how I test it.

Java and Full Stack Instructor

Ran six cohorts end to end at Skolar — curriculum, live sessions, feedback loops.

Skolar · 6 batches × 30 students

Teaching Assistant, Operating Systems

One-to-one debugging at scale across core programming tracks at Coding Ninjas.

Coding Ninjas · 110+ students · 4.9 rating
05 — Impact

Outcomes, not activity.

1,500+

Practitioners onboarded in three months

30,000+

Minutes of incremental watch time

18%

Fewer support requests after roadmap changes

110+

Students mentored, 4.9 average rating

06 — Toolkit

Close enough to the build to be useful.

Product
  • Discovery → delivery
  • Requirement gathering
  • PRDs and specs
  • Acceptance criteria
  • Prioritisation
  • Roadmapping
  • Go-to-market planning
  • Stakeholder management
  • Agile / Scrum
Analysis
  • SQL
  • ReTA
  • Countly
  • KPI tracking
  • Root cause analysis
  • A/B and hypothesis testing
Technical
  • Kotlin, Java
  • REST APIs
  • Node.js
  • RxJava migrations
  • Android TV / Fire TV
  • Database management systems
AI in daily workflow
  • Claude
  • ChatGPT
  • Cursor
  • Spec drafting
  • Requirement synthesis
  • Prompt-led prototyping
07 — Study

Credentials, briefly.

MSc Management — Bayes Business School

London, UK · Distinction · Product Management · Business Analytics · Advanced Strategy Analysis

Sep 2025 – Aug 2026

BE Computer Science — Chitkara University

India · 9.79 / 10 CGPA · Data Structures · Algorithms · Database Management Systems

Aug 2018 – Jul 2022

Product management certifications

Certified Product Management Professional, Beep App · Product Manager, IIBA

2026

Recognition

Gold Medal, Xperi employee recognition (2025) · Winner, ACM Ideathon (2023)

Awards