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Senior ML Engineer
SSC HR SolutionsDescription 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.
Core Competencies
Role fitUse this summary to align your resume positioning with the role.
Demonstrates expertise in applied machine learning and tabular predictive modeling, with a strong focus on building and managing production models for customer prediction and scoring. Proficient in MLOps practices, including model deployment, retraining, and monitoring within a self-managed data platform environment.
ATS Keywords
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About the role
Key responsibilities & impact- 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
What you’ll need- 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.