Senior ML Engineer

DescriptionOwns the predictive customer scores that ship with the product: churn, propensity, lifetime value, spend intent, and response scoring, from training through to monitoring. Applies machine learning and tabular predictive modelling to customer data, building and managing production models that support customer prediction and scoring. Works across the full model lifecycle, including model training, deployment, retraining, monitoring, evaluation, and calibration within a self-managed data platform environment. Supports predictive use cases such as churn, propensity, lifetime value, spend intent, and response scoring, with a focus on models running in production.RequirementsRequirementsApplied machine learning with models running in production, not research or proof of conceptDeep hands on with tabular predictive modelling on customer dataHas built churn or propensity models in telco, banking, or retailTraining, deployment, and retraining pipelines in a self managed environmentMLOps practice: model registry, versioning, retraining, monitoring, and drift detectionComfortable working inside a data platform rather than a notebookUplift or causal modelling for incremental targetingFeature store designWorking with commercial stakeholders on what a prediction is used for