Feature Engineering
The process of transforming raw business data into informative input variables (features) that improve the predictive power of machine learning models for revenue outcomes.
Day-to-day use
Creating time-based features like 'days since last login' and 'usage trend over 30 days' from product event data, normalising CRM field values, encoding categorical variables like industry and employee size, and testing feature importance across model variants.
Typical tasks
- ·Creating usage velocity features for a churn model
- ·encoding industry and company size into model-ready formats
- ·building interaction features that combine engagement and product adoption signals
- ·validating feature importance with SHAP values
Business outcome
Good feature engineering dramatically improves model accuracy — turning raw CRM and product data into the signals that reliably predict which customers churn and which leads convert, making revenue models genuinely actionable.
Where this skill is applied
Technical Disciplines
RevOps AI Engineer
Builds AI-powered tools and workflows that automate revenue tasks, surface insights, and reduce manual work across the sales, marketing, and CS stack.
ML Engineer
Builds machine learning models that power revenue predictions — from churn scoring and lead ranking to pipeline forecasting and pricing optimisation.
RevOps Data Engineer
Designs and maintains the data infrastructure — pipelines, warehouses, and integrations — that ensures revenue data is accurate, available, and actionable.