About the role
Employed clustering algorithms to segment energy customers by usage patterns, enabling targeted marketing campaigns that increased customer engagement by 25% and reduced acquisition costs by $50,000. Catalyzed development of interactive dashboards with Power BI, displaying real-time energy consumption patterns and load forecasting, accelerating critical infrastructure decisions by 30% for key stakeholders. Architected end-to-end data pipelines utilizing SQL, Python, and Apache Spark, accelerating data processing speeds by 40% and enabling real-time analytics across all business units. Identified market trends and consumer behavior via predictive modeling and ML (Scikit-learn, TensorFlow), increasing targeted marketing effectiveness and retention by 20%. Launched a centralized data catalog and dictionary, improving data discoverability and reducing data redundancy by 35%, becoming the single source of truth and enhancing team collaboration. Cooperated with teams, translating complex data insights into actionable business recommendations, driving a 15% reduction in operational costs through strategic process enhancements.