I am a senior operations professional with a finance foundation and a bias toward building. My career has run across B2B SaaS, global commodities, capital markets, and international development — not because I couldn't commit to one track, but because I kept finding that the most interesting problems live at the intersection of fields.

The through line is consistent: I find where manual work is quietly compounding, build the case for fixing it, and ship the solution end to end. That means writing Python when the problem calls for Python. It means walking a skeptical VP through a business case. It means being accountable for what happens after the demo.

I studied Finance at the University of Alberta and I have operated at Acuity Insights, Trafigura, Morgan Stanley, and the UNODC.

Acuity Insights, 2024

Built the business case. Secured stakeholder buy-in, and deployed across customer support.

This is the project I point to when someone asks what it looks like when AI actually works in production — not as a pilot, not as a proof of concept, but as infrastructure.

Python Pydantic Matplotlib Pandas

Pipeline that reads a raw meeting transcript, extracts every action item, classifies each by type and priority, and cross-references against a Revenue Bridge database to auto-escalate tickets involving at-risk enterprise clients. Ships Jira-ready JSON in under a second.

The part I am most proud of is not the classification engine — it is the Revenue Intelligence Gate. A bug blocking a $120K at-risk client is not a "Medium" ticket by any honest measure, and a pipeline should know the difference.

Python Pandas Matplotlib CRM Data

Three-stage pipeline merging CRM, Support, and Product usage data to surface silent churn risk before it becomes a cancellation conversation. Built to catch the accounts that look fine on paper and aren't.

Python Classification NLP

Classification pipeline processing 1,000 enterprise support tickets across 7 categories at 87.2% accuracy. Built to isolate the tickets that cannot wait from the ones that can.

Strategic Framework Enterprise AI Ops Playbook

A deployment playbook for enterprise AI — written from the experience of actually shipping AI in a B2B SaaS environment, not from a policy template. Covers risk classification, behavioral dynamics, triple-gate safety protocols, and what governance actually unlocks commercially.

Senior operations, AI enablement, or strategy roles where the mandate is to build — not just advise. I am most useful to organizations that are past the "should we use AI?" question and into the "how do we do this without breaking what works?" question.

Particularly drawn to high-growth SaaS, fintech, commodity trading, and AI-first companies where the problems are genuinely hard and the pace is high. Open to early and mid-stage startups with ambitious mandates.

Based in Gatineau, QC. Bilingual EN/FR. Open to remote and hybrid.

I have a finance degree and I never fully left the field. These are tools I build on weekends to stay sharp on macro and energy markets — not portfolio padding, just genuine curiosity made visible.

Live sovereign yield curves for US Treasury, Government of Canada, and Euro Area — fetched on page load from three free public APIs (no key required). Tracks 2s10s and 3m10y spreads, inversion signals, and NBER recession bands across a 25-year animated timeline.

Real-time generation mix, demand, and grid stress indicators across ERCOT, PJM, CAISO, and AESO. Tracks renewables penetration, gas dependency, and reserve margin tightness — the conditions that precede price spikes in physical power markets.

Both are single-file HTML tools hosted on GitHub Pages. No frameworks, no backends, no build steps.