{"id":11751,"date":"2026-11-12T16:02:53","date_gmt":"2026-11-12T15:02:53","guid":{"rendered":"https:\/\/www.cfi.ch\/?post_type=postes&#038;p=11751"},"modified":"2026-11-12T16:02:53","modified_gmt":"2026-11-12T15:02:53","slug":"senior-data-scientist-churn-clv","status":"publish","type":"postes","link":"https:\/\/www.cfi.ch\/en\/placement\/postes\/senior-data-scientist-churn-clv\/","title":{"rendered":"Senior Data Scientist \u2013 Churn &amp; CLV"},"content":{"rendered":"<p data-block-type=\"core\"><strong>Experience<\/strong> : 10+ Years &#8211; looking only Immediate joiners<\/p>\n\n\n\n<p data-block-type=\"core\"><strong>Role Summary<\/strong><\/p>\n\n\n\n<p data-block-type=\"core\">Assess the customer\u2019s existing models and recommender capabilities. Define the modelling and measurement strategy for churn, CLV, personalisation, propensity and next-best-offer.<\/p>\n\n\n\n<p data-block-type=\"core\"><strong>Key Responsibilities<\/strong><\/p>\n\n\n\n<p data-block-type=\"core\">\u2022 Review churn models and recommender solutions for performance, reuse and gaps.<br>\u2022 Define targets, features, observation windows and evaluation metrics.<br>\u2022 Develop the CLV methodology.<br>\u2022 Design experiments, control groups and uplift measurement.<br>\u2022 Define explainability, fairness and model-governance requirements.<br>\u2022 Document modelling recommendations and implementation inputs.<\/p>\n\n\n\n<p data-block-type=\"core\"><strong>Must-Have Skills<\/strong><\/p>\n\n\n\n<p data-block-type=\"core\">\u2022 Python, pandas and scikit-learn.<br>\u2022 XGBoost\/LightGBM and PyTorch or TensorFlow.<br>\u2022 Churn, propensity, survival analysis and CLV modelling.<br>\u2022 A\/B testing, uplift modelling and power analysis.<br>\u2022 SHAP, calibration and fairness assessment.<br>\u2022 AWS SageMaker training, Pipelines, Feature Store and Model Registry.<br>\u2022 SQL, Redshift, Athena or Spark.<\/p>\n\n\n\n<p data-block-type=\"core\"><strong>Nice-to-Have Skills<\/strong><\/p>\n\n\n\n<p data-block-type=\"core\">\u2022 Recommender and ranking systems.<br>\u2022 Next-best-offer decisioning.<br>\u2022 Bedrock, LangGraph or similar frameworks.<br>\u2022 Price-sensitivity and constrained optimisation.<br>\u2022 Telecom usage, network and TV-viewing data.<\/p>\n\n\n\n<p data-block-type=\"core\"><\/p>","protected":false},"featured_media":8652,"parent":0,"template":"","meta":{"_acf_changed":true},"class_list":["post-11751","postes","type-postes","status-publish","has-post-thumbnail","hentry"],"blocksy_meta":"","acf":[],"_links":{"self":[{"href":"https:\/\/www.cfi.ch\/en\/wp-json\/wp\/v2\/postes\/11751","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.cfi.ch\/en\/wp-json\/wp\/v2\/postes"}],"about":[{"href":"https:\/\/www.cfi.ch\/en\/wp-json\/wp\/v2\/types\/postes"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.cfi.ch\/en\/wp-json\/wp\/v2\/media\/8652"}],"wp:attachment":[{"href":"https:\/\/www.cfi.ch\/en\/wp-json\/wp\/v2\/media?parent=11751"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}