🕒 3 days ago
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.
Senior ML Engineer
🕒 3 days ago
Machine LearningTabular Predictive ModellingCustomer ScoringChurn Modelling
📜 Description
- Build and manage production machine learning models for customer prediction and scoring.
- Own model training, deployment, retraining, monitoring, evaluation, and calibration.
- Develop predictive scores for churn, propensity, lifetime value, spend intent, and response.
- Operate models within a self-managed data platform environment.
- Work with commercial stakeholders to define how predictions are used.
🛠️ Requirements
- Applied machine learning with models running in production, not research or proof of concept.
- Deep hands on with tabular predictive modelling on customer data.
- Has built churn or propensity models in telco, banking, or retail.
- Training, deployment, and retraining pipelines in a self managed environment.
- MLOps practice: model registry, versioning, retraining, monitoring, and drift detection.
- Comfortable working inside a data platform rather than a notebook.
- Uplift or causal modelling for incremental targeting.
- Feature store design.
- Working with commercial stakeholders on what a prediction is used for.
Full job description
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
- Applied machine learning with models running in production, not research or proof of concept.
- Deep hands on with tabular predictive modelling on customer data.
- Has built churn or propensity models in telco, banking, or retail.
- Training, deployment, and retraining pipelines in a self managed environment.
- MLOps practice: model registry, versioning, retraining, monitoring, and drift detection.
- Comfortable working inside a data platform rather than a notebook.
- Uplift or causal modelling for incremental targeting.
- Feature store design.
- Working with commercial stakeholders on what a prediction is used for.
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