The practitioner who makes AI actually work in your organization.
BrightRoute Analytics was founded by a practitioner who saw a clear opportunity: to equip businesses of all sizes with AI that actually delivers. We turn that potential into practice — with rigorous problem scoping, solid data foundations, and a relentless focus on measurable results.
Rafael Falcon
Founder & Principal Data Scientist
Ottawa, Canada · PhD in Computer Science
Rafael is a full-stack data scientist with 15 years of combined experience in the commerce and security/defense sectors. Before founding BrightRoute Analytics, he spent 8 years at Shopify as a Senior Product Data Scientist, where he was the technical lead for a team of 10 data scientists and data engineers supporting the Deliver and Merchandising product areas.
At Shopify, Rafael built production data pipelines processing 100M+ raw data points daily, developed systems for marketing uplift modeling, anomaly detection, and causal analyses, and designed the end-to-end product analytics framework that helped shape Shopify Back Office's strategic product roadmap.
Prior to Shopify, he spent 6 years as a Research Scientist at Larus Technologies, designing and deploying algorithmic solutions for maritime domain awareness — including unsupervised anomaly detectors and multi-sensor fusion systems — for DRDC Ottawa, DRDC Valcartier, and the Canadian Navy. He also supervised graduate students as an Adjunct Professor at the University of Ottawa.
Rafael founded BrightRoute Analytics around a simple thesis: rigorous problem scoping and data foundations are more important than model sophistication. Start with the business question. Build the infrastructure to answer it reliably. Then — and only then — build the model.
Our values in practice
Rigor over rhetoric
We don't hype AI. We scope problems carefully, validate assumptions, and only recommend approaches where we can demonstrate measurable ROI.
Business outcomes first
Every technical decision is evaluated through the lens of business impact. Model accuracy matters — but only insofar as it moves a KPI that matters to your P&L.
Transparent about uncertainty
We tell clients what we don't know and where models might fail. Trust is built by being honest about limitations, not by overpromising.
Knowledge transfer by design
We aim to build your internal capability — not create dependency. Every engagement includes documentation, training, and capability-building as a first-class deliverable.