July · Curling vision
Read the scene. Explain the play.
A physical model of the ice connects stone locations to a reviewable tactical story.
Explore the case studyData · Quant · AI leadership
I lead data products from research to reality.
I connect quantitative research, computer vision and data platforms to products people can use. Recent work spans WNBA advisory systems, sports-scene understanding and reporting that makes its evidence clear.
10+ yearsApplied data and modelling
0 → 1 → runStrategy through operation
PhD mathematicsRigour without theatre
Jordan Moore, PhD
Lead Quant · Head of Technology & AI · CTO
Evidence in motion · 2026
See the work behind the result.
Scroll through four new stories: scene understanding, live model operations, trustworthy reporting and a closer look at model errors.
August · Quant research
Make the model operable.
Collection, evaluation and explicit promotion support a read-only WNBA advisory product.
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September · Data leadership
Make every view mean the same thing.
Shared metric definitions, complete records and visible uncertainty turn reporting into a dependable product.
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September · Snooker vision
Inspect what the model gets wrong.
A frozen comparison shows fewer misses, persistent extras and why annotation quality matters.
Explore the case studyJuly–September 2026
Leadership you can inspect.
New case studies in computer vision, quantitative research and the work of leading dependable data products.
2026-08 edition · sports intelligence
Running a live WNBA model
The collection, evaluation, promotion and operating cycle behind a read-only WNBA advisory product.
2026-09 edition · sports intelligence
What the snooker model gets wrong
A frozen snooker model comparison reveals fewer misses, persistent extra detections and the importance of reviewing the labels.
2026-09 edition · product leadership
A dashboard should earn your trust
Building reporting people can rely on: consistent cohorts, complete records, visible uncertainty and a dependable read path.
Latest visual demo · Curling
Computer vision that tells a story.
From house geometry and stone positions to a tactical explanation. Watch the recorded sequence, then inspect the evidence behind it.
How I lead
Build the operating system, not just the model.
My job is to make a technical team more decisive: clear interfaces, explicit quality gates and evidence that survives the move from notebook to production.
Start with the decision
Define who acts, what changes and what evidence earns promotion before choosing the model or platform.
Make quality reviewable
Golden sets, failure modes, shadow runs and clear readouts turn “looks promising” into an accountable release decision.
Design for operation
Ownership, telemetry, runbooks and stakeholder language are part of the product—not clean-up work for later.
Current portfolio
Embedded where data meets the decision.
Lead Quant Researcher
Research governance, market and wallet signals, challenger models, monitoring and the path from backtest to live operation.
Head of Technology & AI
Sports-data annotation, QA infrastructure, computer-vision challenges and model-ready datasets.
CTO
AI sports-content architecture across ingestion, retrieval, generation, evaluation and editorial review.
Build something dependable
Need a data product that can earn trust?
I work with teams at the point where technical possibility needs to become a clear operating decision.