What you’ll deliver: - Data Science & Modeling: develop, validate, and deploy statistical and machine learning models; perform exploratory data analysis to identify trends, anomalies, and opportunities; build predictive models (revenue forecasting, member retention, segmentation); design and evaluate experiments (A/B testing where applicable); develop feature engineering across large datasets. - Business & Analytical Translation: translate business requirements into analytical models and measurable outputs; define key metrics and KPIs; provide data-driven recommendations; support financial, marketing, and operations teams with advanced analytics. - AI & Advanced Analytics: support AI initiatives including recommendation models, automation, and agents; apply techniques such as regression, classification, clustering, time series forecasting, and NLP where applicable; evaluate model performance and optimize. - Data Integration & Collaboration: work closely with Data Engineers to ensure data readiness and pipeline integrity; collaborate with BI Engineers to productionize models into reporting layers; ensure models integrate into enterprise data architecture; partner with QA to validate data

What you’ll deliver: - Data Science & Modeling: develop, validate, and deploy statistical and machine learning models; perform exploratory data analysis to identify trends, anomalies, and opportunities; build predictive models (revenue forecasting, member retention, segmentation); design and evaluate experiments (A/B testing where applicable); develop feature engineering across large datasets. - Business & Analytical Translation: translate business requirements into analytical models and measurable outputs; define key metrics and KPIs; provide data-driven recommendations; support financial, marketing, and operations teams with advanced analytics. - AI & Advanced Analytics: support AI initiatives including recommendation models, automation, and agents; apply techniques such as regression, classification, clustering, time series forecasting, and NLP where applicable; evaluate model performance and optimize. - Data Integration & Collaboration: work closely with Data Engineers to ensure data readiness and pipeline integrity; collaborate with BI Engineers to productionize models into reporting layers; ensure models integrate into enterprise data architecture; partner with QA to validate data