Most analytics engagements end with a presentation and a new dependency. This one ends with a product your team owns — built simply, delivered in a week, and designed to last.
Free Consultation
No cost · 1 hour
An honest conversation about your data environment, where you want to go, and whether there's a fit. No pitch.
Paid Assessment
$2,000
A tailored, actionable roadmap of what to build and in what order. Concrete. Yours to keep — whether you'd like me to do the work or not.
Sprint Engagement
Per tier · One week
I build the defined deliverable and demo it at the end. You get working output — not a slide about future output. I'll provide recommended next steps. You can hire me for those or end the engagement. No pressure.
Ongoing Retainer
Monthly · By arrangement
Maintenance, monitoring, and continued development for clients who want the work to keep compounding.
Pricing is scoped to the engagement, not the clock. Start with a free consultation and we'll figure out what makes sense.
As a solo practitioner, there's no firm overhead, no account managers, and no junior staff doing the work. You work directly with the person doing the work — and that's reflected in the price.
No perfect infrastructure required. No clean data required.
One person handles the full stack — raw data → analytics pipeline → ML model (if needed) → actionable dashboard or data product — with no handoffs and no gaps.
Senior-level output in a compressed timeframe, explained in plain language your team can actually build on.
Every sprint ends with a working analytics product. Not a PowerPoint.
15+ years embedded in organizations at every stage of their analytics journey — building from scratch, untangling what wasn't working, and finding what's possible within real constraints.
The consistent finding: most analytics work gets more complicated than it needs to be. The goal here is the opposite — the simplest solution that actually solves the problem, built so your team can own it when the engagement ends.
That means honest counsel on what you actually need: when a dashboard gets you there, when machine learning is worth it, and what gets you to a useful baseline fast. The handoff is part of the work, not an afterthought.
Built many of the foundational models, infrastructure, and patterns that teams at Casey's, John Deere, and the State of Iowa still rely on today
Global sales lead analytics at John Deere — adopted by 80% of North American dealerships
Rebuilt broken consultant models (price elasticity, fuel routing) and made them work
Designed the system architecture to integrate data across disparate government systems and blend with public data sources — making analytics possible in areas where the data complexity had previously made it impractical
ML deployment frameworks that cut time-to-production by 90%
John Deere 2019 President's Award
Python · R · SQL · DBT · DuckDB · MLFlow · LangGraph · Docker
Databricks · AWS · Azure · Tableau · Power BI · GitHub
Raw data → Analytics pipeline → ML model (if needed) → Actionable dashboard or data product — containerized for easy handoff and deployment