I’m a product and commercial strategy professional with 10+ years of experience across telecom, FMCG/CPG, technology, and retail, including global organizations such as Unilever and JTI.
My experience spans product strategy, go-to-market, P&L ownership, digital transformation, customer strategy, and cross-functional leadership.
More recently, I’ve been deepening that experience through hands-on work in AI product management, product experimentation, AI evaluation, and AI product strategy.
This GitHub is my product portfolio — a collection of case studies showing how I approach:
Problem → Evidence → Product Decision → Experiment → Business Outcome
The challenge
How can a fintech product improve trial-to-paid conversion without asking users to pay before they experience meaningful value?
My product bet
Move the paid boundary after the user reaches a personalized Aha moment: seeing a 30-day cash-flow risk and reviewing a recommended action.
What I worked on
PLG Strategy · Aha Moment · A/B Testing · MDE · Guardrails
· Product Metrics · Monetization
Experiment
Test whether presenting the paid plan after personalized value increases trial-to-paid conversion from 1.99% → ≥2.4%, while protecting engagement.
👉 Explore the FinWise case study
The challenge
RouteLogic grew into a powerful enterprise logistics platform, but increasing product complexity began slowing the frontline users who depend on it for time-critical operational decisions.
Research showed coordinators moving work into WhatsApp, spreadsheets, calls, screenshots, and other manual workarounds when the platform became too slow or unreliable.
My product bet
RouteLogic became powerful by adding more. Velocity tests whether it can become more valuable by knowing what to remove.
What I worked on
Product Discovery · UXR Synthesis · JTBD · Journey Mapping
· Prioritization · PRD · Experimentation · Roadmap · GTM
Strategic objective
Restore RouteLogic as the frontline system of action while preserving enterprise capabilities in the background.
👉 RouteLogic case study — coming soon
The challenge
How can AI improve CPG demand and promotion planning without asking planners to trust recommendations the underlying evidence cannot support?
Strategic direction
Use AI selectively in high-value planning decisions where proprietary data, workflow integration, human oversight, and measurable outcomes can create a defensible advantage.
What I worked on
AI Strategy · AI Value Proposition · Data Advantage
· Human-in-the-Loop · Evals · Unit Economics
· Guardrails · AI Governance
North Star
Make ShelfSense the trusted decision layer for CPG demand planning.
👉 ShelfSense case study — coming soon
I believe strong product management connects four questions:
| Question | |
|---|---|
| 👤 Customer | What problem is actually worth solving? |
| 🎯 Strategy | Why should we solve it — and why now? |
| 📊 Evidence | What would prove or disprove our assumptions? |
| 💼 Business | How does solving it create sustainable value? |
AI can accelerate research, synthesis, prototyping, and execution.
Product judgment still determines what should be built, what should not, and what evidence is strong enough to make the decision.
My experimentation approach starts before a feature is built:
Hypothesis → Primary Metric → MDE → Guardrails → Experiment → Decision
I focus on defining success before seeing results and distinguishing between:
- Statistical significance and business significance
- Product activity and genuine user value
- Leading signals and business outcomes
- Correlation and evidence strong enough to support a decision
My AI product work focuses beyond simply adding AI features.
I explore questions such as:
- Where does AI create meaningful user value?
- When should AI recommend versus act autonomously?
- How should product teams evaluate quality before scaling?
- What failure modes could damage user trust?
- Where can proprietary data create defensibility?
- When do AI unit economics support the product strategy?
- Where must humans remain in the decision loop?
Product Strategy Discovery JTBD UXR Synthesis
Prioritization Roadmaps PRDs GTM
Hypothesis Design A/B Testing MDE
Guardrails Product Metrics Decision Criteria
AI Product Strategy AI Evals Human-in-the-Loop
AI Guardrails Prototyping
P&L Category Strategy Customer Strategy
Go-to-Market Stakeholder Management S&OP / IBP
🎓 Product Management Certification — Product School
🎓 AI Product Management Certification — Product School
🎓 Product Experimentation Certification — Product School
🎓 AI Evals
📖 AI Product Strategy — In Progress
🏅 PMP®
The goal isn't to collect certifications.
It's to combine modern product and AI practices with the commercial experience I've built throughout my career.
Each major repository is structured as a product case study rather than simply a collection of deliverables.
I document:
01. Problem — What are we solving?
02. Evidence — What do users and data tell us?
03. Insight — What did I learn?
04. Product Bet — What decision did I make?
05. Execution — What did I design or prototype?
06. Validation — How would I test it?
07. Business Impact — Why does it matter?
08. Reflection — What would I change or investigate next?
I'm particularly interested in opportunities at the intersection of product strategy, AI-enabled products, experimentation, growth, and digital transformation.
📍 Calgary, Alberta, Canada