senior-data-scientist
Build prediction models with statistical checks.
What it does
Guides the creation of experiment analyses and predictive-model pipelines with uncertainty estimates and validation checks.
When to use it
Use when designing or analyzing experiments, building predictive models, engineering tabular features, or estimating causal effects from observational data.
How to use it
It changes how the agent implements data-science work in project code, prescribing statistical checks, feature-processing rules, and model evaluation practices.
What you provide
- An existing project
What you need · 8
Python 3 runs the bundled pipeline scaffolds.
The experiment-analysis and time-feature code imports NumPy.
The A/B-test code imports SciPy statistics for sample sizing and significance calculations.
The feature-engineering code imports Pandas to process tabular data and datetime columns.
Access · 0
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