Experience : 10+ Years – looking only Immediate joiners
Role Summary
Assess the customer’s existing models and recommender capabilities. Define the modelling and measurement strategy for churn, CLV, personalisation, propensity and next-best-offer.
Key Responsibilities
• Review churn models and recommender solutions for performance, reuse and gaps.
• Define targets, features, observation windows and evaluation metrics.
• Develop the CLV methodology.
• Design experiments, control groups and uplift measurement.
• Define explainability, fairness and model-governance requirements.
• Document modelling recommendations and implementation inputs.
Must-Have Skills
• Python, pandas and scikit-learn.
• XGBoost/LightGBM and PyTorch or TensorFlow.
• Churn, propensity, survival analysis and CLV modelling.
• A/B testing, uplift modelling and power analysis.
• SHAP, calibration and fairness assessment.
• AWS SageMaker training, Pipelines, Feature Store and Model Registry.
• SQL, Redshift, Athena or Spark.
Nice-to-Have Skills
• Recommender and ranking systems.
• Next-best-offer decisioning.
• Bedrock, LangGraph or similar frameworks.
• Price-sensitivity and constrained optimisation.
• Telecom usage, network and TV-viewing data.