H.E.B, Inc. – Senior Data Scientist (February 2020 – Present)
- Generative AI Innovation: Pioneered the development of multiple AI-powered systems including an automated expense classification system using agentic RAG and LangGraph for credit card transactions, and an intelligent documentation assistant leveraging LangChain, OpenAI, and Claude AI. These innovations significantly improved operational efficiency, regulatory compliance, and knowledge accessibility across the organization.
- Large-Scale Optimization Leadership: Spearheaded a transformative assortment space optimization initiative, migrating from SQL to Spark-based architecture with multi-stage predictive modeling (global/local XGBoost) and linear programming. This strategic overhaul revolutionized store layouts and drove an impressive 8% revenue increase.
- Advanced Forecasting Excellence: Led multiple high-impact forecasting projects including a warehouse item-class demand forecasting system on Databricks achieving 40% accuracy uplift, and a cutting-edge forecasting engine using Ray and Vertex AI on GCP that reduced errors by 9% (15% for promoted items) while enhancing inventory efficiency by 5%.
- Platform Transformation & Efficiency: Transformed the promotional forecasting platform from R/Scala to PySpark distributed computing on AWS, achieving a 2% error reduction and remarkable 70% runtime improvement. Additionally, developed PipeLearner, a Spark-distributed package that revolutionized model deployment by cutting training time by over 50%.
- Price & Promotion Optimization: Directed multiple optimization projects combining predictive modeling (XGBoost, Linear models) with prescriptive analytics (linear programming, meta-heuristics), resulting in 5% revenue increase for markdown products while significantly reducing stockouts and product expiration.
University of Texas at San Antonio – Research Assistant/Instructor/Teaching Assistant (2016 – 2019)
- Educational Impact: Enhanced predictive accuracy in educational research through the application of advanced statistical and machine learning techniques, contributing significantly to the development of student success strategies.
- Cybersecurity Advancements: Conducted groundbreaking cybersecurity research within a manufacturing context, employing a game theory approach that bolstered security measures and data protection protocols.
- Operational Efficiency: Improved shop floor scheduling efficiency in a virtual manufacturing environment by utilizing sophisticated optimization algorithms, showcasing my ability to apply data science methodologies for operational enhancements.
Reef Chemical Industries Complex – Manufacturing/Enterprise Data Analytics Manager (2013 – 2015)
- Predictive Maintenance Model Implementation: Developed and implemented predictive maintenance models that markedly improved operational efficiency and equipment lifespan, demonstrating my ability to leverage data science for tangible business improvements.
- Data Infrastructure Development: Designed and established comprehensive data structures for operations tracking, significantly boosting data analysis productivity and informed decision-making capabilities.
Isfahan Steel Complex – Data Analyst/Consultant (2009 – 2013)
- Maintenance System Optimization: Led the adoption of an optimized maintenance system, which played a critical role in reducing unplanned downtime and enhancing machinery health forecasting, underlining my proficiency in applying data analysis for operational excellence.
- Emergency Maintenance Data Analysis: Employed statistical analysis techniques to scrutinize emergency maintenance data, achieving a notable reduction in emergency repairs and underlining the value of data-driven insights in crisis management and prevention.