Principal Data Scientist

The responsibilities name model degradation, data drift and concept drift specifically, which is a good sign that shipping is expected to be followed by maintenance. Customer propensity, product forecasting and recommender systems, owned end to end from requirements through production monitoring.

Working with data engineering and MLOps to operationalise, and engaging directly with decision makers across the business. Classic applied data science rather than research. US remote, Python and SQL.