Product Manager

Right problem. Real product. Shipped.

I'm a Product Manager who takes products from the first PRD to running in production, and writes the code for my own on the side. Four sectors and twenty-plus markets have taught me one thing that carries across all of them: the real problem usually sits with whoever has to use the product, not whoever asked for it. That's where I start.

Working across the product lifecycle

Product Discovery
User Research
Roadmap & Prioritisation
Data & Analytics
AI & Data Products
Prototyping
Build & Run
Stakeholder Alignment

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4+ years in product and analytics
4 sectors life sciences, e-commerce, beauty, industrial
20+ markets worked across
Paris & remote open to roles internationally

The roles I cover

Most teams split these titles between different people. I have carried all four in the same week, which is the argument for hiring someone who has done each of them rather than one of them neatly.

Product Manager
Roadmaps

Prioritising, sequencing, and keeping a room full of opinions pointed the same way. Choosing what gets built next, then holding that line when someone senior wants to jump the queue.
Product Owner
Backlogs

PRDs, user stories, refinement. Turning a vague ask into something an engineering team can pick up on Monday morning without asking me three questions first.
Project Manager
Governance

Test plans, sign-offs, cutover checklists and the monthly numbers. The unglamorous rhythm that tells you where a programme actually is, rather than where the status slide says it is.
Business Analyst
Fieldwork

Interviews, shadowing, and funnel data read alongside them. Where somebody gets stuck says far more than what they tell you they want.

How I make the call

Four habits that decide what I do when the evidence is thin and somebody wants an answer today.

Name the problem first

A feature request is somebody's guess at a solution. I work backwards from it until the problem fits in one sentence, and only then talk about what to build.
Buy certainty cheaply

A prototype, a pilot and four honest conversations cost less than a quarter of engineering time. Better to break an idea while it still takes a week to fix.
Make the trade-off visible

Every real decision gives something up. I'd rather write down what we're sacrificing and defend it than ship a compromise nobody actually chose.
Judge it by what moved

Shipping on time is where the question starts. What counts is whether people use it, whether it survives production, and whether a number anyone cares about changed.

Capabilities and toolkit

What I actually use, grouped by the job it does.

Product: Discovery
PRDs and user stories
Roadmap and backlog
Prioritisation
Experimentation and A/B testing
KPI and OKR design
UAT
Rollout and change management
Build and run
Stakeholder management
Data and analytics: SQL
Python
Power BI
Tableau
Looker
Excel
AI: LLM-powered MVPs
Prompt engineering
AI agent deployment
Fuzzy matching
Platforms and tools: Jira
ServiceNow
Figma
AWS
GCP
Git
Building: JavaScript
Three.js
Web Audio
Offline-first PWAs
Lovable
Replit
Glide

Work

Case studies and projects. Each one goes into how the problem was framed, the trade-offs, the architecture where there is one, and what actually changed, across AI, B2B, data and consumer products. Every write-up includes the part I'd do differently.

01 Case study GTM strategy 2026

Where should an AI materials company go first?

Market entry for a generative-AI materials discovery platform

Problem statement
When your technology applies to a dozen industries, the hard question stops being 'can we' and becomes 'which one, and why that one first'.

My role

Market analysis and go-to-market recommendation

Key outcome

Mapped data centre cooling into a five-layer value chain, narrowed twelve materials opportunities to one through three filters and a weighted scorecard (PFAS-free direct-to-chip coolants, 4.7 out of 5), and showed why Entalpic can't sell it directly and has to route through hardware vendors and fluid OEMs instead.
- Go-to-market
- Market sizing
- Deep tech
- Channel strategy
02 Case study Data product 2026

What does a Paris flat actually return once the rent cap applies?

A pipeline that joins sale prices to legal rent ceilings

Problem statement
An investor can find out what a flat costs in about ten seconds and can't find out what it returns at all.

My role

Product concept, data pipeline and investor tool

Key outcome

Joined three open French datasets (DVF, DPE, DRIHL) into 8,960 rent-zone records, applied the legal cap to every recorded transaction, and built a ranked table and map of cash-on-cash returns by rooms, budget and arrondissement. Match rate of 58% is documented, not hidden.
- Geospatial data
- Python
- Open data
- Investor tool
Read the full case study →
- Repo
Launch video
03 Project Marketplace 2025

Finding a co-founder shouldn't come down to who you sat next to

Founder-teammate matching that scores for complementarity, not similarity

Problem statement
I can't quickly find someone who complements me, is serious about it, has time, cares about the same problem, and wants the same kind of arrangement I do.

My role

Product owner and builder, PRD to live

Key outcome

Rules-based matching with explicit weights (role fit 30%, commitment 25%, proof of work 20%, industry 15%, working style 10%), hard filters above the score, and a reason shown for every match. Live on seeded data, built for a real cohort pilot.
- Matching
- Explainable scoring
- Two-sided marketplace
- PRD
Read the full case study →
- Live
- Repo
Launch video
04 Project Consumer / Ed-tech 2026

PM interview prep you can do on the metro

An offline-first coach that works underground, with no signal and no account

Problem statement
Reading isn't practising. And nobody revises at a desk with a stable connection. They revise on the way somewhere.

My role

Product, content and full build

Key outcome

One 1.9 MB file holding 27 chapters, 68 scored practice questions and a 352-term glossary on spaced repetition, precached by a service worker so an offline load renders byte-identical. All colour pairs audited to 4.5:1 or better, every tap target at least 44 px.
- Offline-first PWA
- Spaced repetition
- Accessibility
- Vanilla JS
Read the full case study →
- Live
- Repo
Launch video
05 Project Game design 2026

What does a 1996 arcade shooter teach you about product?

An on-rails light gun game, rebuilt from scratch in one HTML file

Problem statement
Arcade games had three minutes and one coin to teach you the rules, make you care, and make you want to play again. Most products get more than that and do worse.

My role

Design and full build

Key outcome

2,325 lines, one file, zero assets. Every model is built from Three.js primitives at runtime and every sound is synthesised. Kept the arcade grammar (shrinking threat reticles, hit zones, a three-phase boss) and changed the incentives: disarming an enemy pays more than shooting them, and civilians in the line of fire cost you.
- Three.js
- Game design
- Procedural
- Systems design
Read the full case study →
- Play
- Repo