Technical: Proficiency in SQL and programming techniques and tools (Python, R); Cloud computing platforms: Azure, Snowflake; Machine Learning and Advanced Analytics: predictive modeling, classification, regression, clustering, feature engineering, experiment design, model validation, and performance optimization; NLP skills including tokenization, sentiment analysis, and embeddings; Model Optimization: model tuning, hyperparameter adjustment, explainability, bias and fairness testing, reproducibility, monitoring, drift assessment, and model lifecycle management; Experience collaborating with Data Engineering, IT, Architecture, DevOps/MLOps, Product, and business stakeholders within Agile or Product-Oriented Delivery (POD) environments. The role translates business challenges into mathematical and statistical models, works with Data Engineering and IT partners to prepare enterprise data, and partners with Enterprise Technology AI Platform, MLOps, Architecture, Security, Compliance, and Operations teams to move governed solutions through the AI/ML lifecycle.