Advisory

Advisory is the work I do inside an institution's own data, models, and decisions — sharpening how macro analysis becomes a decision in your context, not in the abstract.

The Work

The engagement takes the shape the problem needs.

Sometimes it's a review of an existing macro and forecasting framework — what's working, what isn't, and where it's quietly leading you wrong. Sometimes it's building a data and modeling approach from scratch, or rebuilding one that's outgrown itself. Sometimes it's ongoing macro counsel — a standing, model-driven view to test your own thinking against.

Increasingly it's bringing AI into the workflow: finding where machine-learning and AI tools genuinely improve how macro is modeled and used, and where they don't.

The Proof

This is work I've done before, for institutions like yours. I've advised multiple central banks on building their macro modeling frameworks, and worked with Fortune 500 companies across natural resources, retail, and finance on designing their data and modeling architecture. All of it rests on having built and run these systems for real — as CIO of a global macro hedge fund, where the models carried positions and risk, not just analysis.

The Engagement

What makes it advisory rather than research is simple: in an advisory engagement, I'm working inside your data and models, under NDA. Research is the reverse — I deliver my own outputs.

Engagements are scoped and bespoke — a real working relationship, not hours on a clock. At its deepest, that can mean an ongoing seat: advising a team, or a board, as macro conditions keep shifting.

If you're rethinking how macro feeds your decisions, that's the conversation to start.