Data Science & Forecasting
Forecasting and causal inference, vouched for by data leaders at Danone, Kraft Heinz, and Franklin Sports.
This is the work behind the testimonials — demand forecasting, causal inference, and predictive modeling — in three shapes you can actually put a budget against.
Kraft Heinz
Head of Global Forecasting
Danone
Data Scientist
Franklin Sports
VP of Data Analytics
The offer
Three shapes this engagement can take
Audit
Forecasting Audit
A focused review of your current forecasting or attribution approach: where the error is coming from, and whether a model is even the right fix.
Leave with: a written diagnosis and a build-vs-buy recommendation.
Fractional
Fractional Data Scientist
Ongoing part-time capacity for teams that need senior forecasting or causal inference work but aren’t ready for a full-time hire.
Leave with: a maintained model, monthly reporting, and a team that knows how to read it.
Project
Scoped Model Build
A fixed-scope build for one specific problem — a demand forecast, an attribution model, a lead-scoring pipeline — from data audit to handoff.
Leave with: a production-ready model, documentation, and a handoff session.
Not ready to book?
Get the forecasting-readiness checklist
A short, practical checklist for knowing whether your data is ready for a forecasting model — before you spend on one.
Objections, answered
Common questions before a data science engagement
How much accuracy improvement should we expect?
Should we build our own model or hire this out?
How long until we see value?
How much historical data is actually enough?
What if our data isn't clean yet?
Bring the problem. I’ll scope the engagement on the call.
Opsis Data
Forecasting, causal inference, and automation for teams who need proof, not decks.
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Data Scientist · Analytics · Forecasting