Data Science — Models That Ship, Not Notebooks That Impress
Most data science work dies in a Jupyter notebook: a promising model with strong offline accuracy that never makes it into production, or worse, one that ships without proper validation and quietly degrades. ZiroData builds predictive and optimization models with the same engineering discipline as the systems they’ll run inside — versioned, monitored, and validated against real outcomes over time.
The Problem With Most Data Science Projects
Models are validated once, at build time, with no monitoring for drift as real-world data shifts away from training data.
– Offline accuracy metrics don’t match business impact — a model can be 95% accurate and still be useless if it’s wrong on the cases that matter most.
– “We need machine learning” is assumed before checking if a simpler statistical model or rule would solve the problem at a fraction of the cost and complexity.
– Models get built without a deployment plan, so a working prototype has no path into a real system.
We start every engagement by asking whether the problem actually requires machine learning, or whether a simpler, more maintainable approach solves it just as well.
How This Connects to the Rest of Your Data Stack
Data Science provides the modeling engine for: - Growth Intelligence — >forecasting and experiment design - Customer Intelligence — >churn prediction and CLV modeling - Decision Intelligence — >the underlying models behind scenario and risk analysis
What's Included
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Your Guide to Common Questions & Solutions
How is Marketing Intelligence different from a marketing agency?
An agency runs channels. We build the measurement and modeling layer that tells you — and any agency you hire — what’s actually working.
Do you replace our existing marketing team?
No. We build the intelligence layer they use to make better decisions, and train them to run it.
How long until we see results?**
Initial attribution and reporting infrastructure: 3–6 weeks. Marketing Mix Modeling with statistically reliable output: typically requires 12+ months of historical data.