Description
Owns 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.
Requirements
Requirements
Lead end-to-end machine learning development, from formulating complex modeling problems and testing advanced approaches to production deployment, performance evaluation, and guiding scientific practices across teams.
Build and deploy production machine learning systems that improve supply chain forecasting, inventory and cost optimization, and supplier risk decisions, while shaping the AI roadmap and modeling standards.
Set machine learning direction and deliver production LLM systems, from post-training and agent workflows to evaluation and dialogue improvements that shape user experience, retention, and monetization.
Build and operate production machine learning systems across the full modeling lifecycle, from framing business problems and engineering features to deploying, monitoring, and improving models at scale.
Develop and productionize machine-learning and computer-vision solutions that turn noisy real-world data into accurate traffic-control map features, improving detection, positioning, road association, and map quality.
