• Design, train, and evaluate predictive and optimization models using statistical, machine learning, and deep learning techniques. • Build and deploy models for classification, regression, clustering, and anomaly detection. • Develop time series forecasting models for churn prediction and demand forecasting. • Balance R&D prototyping with production-grade model development. • Partner with data architects and engineers to source, clean, and transform large datasets from the Fabric data lakehouse and warehouse environments. • Create reusable data pipelines and feature stores for model training and scoring. • Ensure model reproducibility, explainability, and integration into downstream reporting or applications. • Use Python extensively with libraries such as scikit-learn, pandas, numpy, statsmodels, seaborn, and matplotlib. • Work with SQL and T-SQL for data manipulation and analysis. • Create compelling visualizations and dashboards using Power BI including DAX measures. • Partner with BI analysts, data engineers, and architects to understand business needs. • Translate complex modeling outcomes into actionable business recommendations. • Present findings and model outcomes to non-technical stakeholders across Product, Operations, and Leadership. • Contribute to the development of scalable data science infrastructure and best practices.