Enterprise Solutions
The decision-science team you don’t have to hire.
You have data — maybe even a BI team. What’s missing is the decision-science layer that turns data into optimized pricing, scenario-based forecasts, and tested decisions. I build that layer, and I design it to be owned by one person.
Built for teams that know the gap.
You’re a six-, seven-, or eight-figure business, and you know exactly where you need help — acquisition-source optimization, a rolling forecast that updates itself with actuals, a real test for that price change. But the need doesn’t justify hiring and onboarding a full analytics team. That’s the gap I fill: enterprise-grade decision science, built to be maintained by a single owner.
How I work
Every engagement comes with a number.
If we can’t measure it, we won’t sell it.
We agree on the KPI that means success for your business, build to move it, and keep it visible — so results are never in question.
Together we pick the one metric that defines success.
The site, tool, or system designed to move it.
The number stays visible — your proof, and mine.
What I build for enterprises
Scoped to your data maturity and goals. Where an analytical team exists, I extend it; where one doesn’t, I stand up the layer a single owner can run.
Decision science & optimization
Optimal pricing from relative elasticities, margin-outcome variation, and holding costs; segmentation that maximizes total contribution; acquisition-source optimization across channels.
Forecasting & scenario planning
Rolling forecasts with scenario planning, not single outcome estimates — built on seasonality, YoY performance, and sentiment, then actualized automatically each period. Maintained by one person, not a team.
Data & systems infrastructure
Querying and visualization on your stack (Snowflake, Tableau, Python, Streamlit) and database design with real audit trails — e.g. a Streamlit UI that writes to a queryable orders table.
Testing & experimentation
A/B and split tests that deliver rigorous reads on fundamental questions — retail pricing, stocking levels, product breadth, and conversion — wherever the data infrastructure exists to run them.
There’s no fixed menu — every engagement is priced to the work. Let’s talk specifics.
Where we’re headed
From decisions to autonomous systems.
On the roadmap: agentic AI that monitors macro-market shifts for supply shocks; BI command centers that unify inventory, conversion, returns, and forecast variance into one view; and full experimentation infrastructure — not just test design, but the systems to run the tests at scale.
Let’s find your number.
Tell me where you want more from your data, and we’ll define the metric worth moving first.