Creator of Rankings Right Now, a machine learning based web application that predicts college football rankings in real time.

- Managed team of three machine learning engineers, data engineer, data analyst and ML intern to build ML pipelines using neural networks, NLP, XGBoost, clustering, GNN and ALS recommendation models- Lead machine learning teams for digital marketing campaigns and yield optimization of impression bids- Created user embeddings based on viewership habits to fuel other downstream machine learning models like brand affinities and estimated political affiliations- Designed scalable pipelines to handle tens to hundreds of digital marketing campaigns for targeting on CTV and mobile platforms- Architected NLP project to categorize TV episodes into clusters for viewer affinity associations used in digital advertising campaigns- Implemented A/B testing architecture for digital marketing campaigns for flexible analysis and data tracking for downstream statistical analysis- Optimized machine learning data model pipelines using Snowflake DBMS and Databricks Spark platform handling hundreds of millions to tens of billions of records on a nightly basis- Engineered and visualized simulation results derived by Markov Chain and advanced probabilistic models estimating campaign performance under specified constraints- Implemented automated campaign manager tool to track health of all digital marketingcampaigns and make automatic adjustments to fulfill campaign goals

• Created time series forecasts and logistic regression machine learning models using R to predict future in game spending trends• Theorized and computed dynamic game strategies with cluster and predictive modeling• Worked together with game developers to productionize machine learning experience classifications from telemetry data back into the game for enhanced user experience• Significantly improved automated reports and visualizations by writing dynamic SQL into Python scripts for sustainable database architecture• Generated reports on user behavior in different game modes to segment different user type behaviors• Assisted in ETL pipelines and dynamic scripts using Python, MySQL and AWS Redshift handling billions of daily telemetry data points• Developed Tableau reports analyzing new game modes and interest points for the user experience team• Collaborated with Live team in promotional sales to drive user engagement

• Designed machine learning models using Python and Docker images for scalable machine learning solutions in the education and sports & entertainment sectors• Work with machine learning packages Scikit-Learn, Keras, Pandas, Numpy and Matplotlib• Implemented classification programs to predict retention rates, prospect identification, likelihood to donate, and probability of attending an event• Applied regression algorithms for identifying future large donors for universities

• Engineered and implemented a database application to handle sponsorship data and communicate with front end functionality for two of the largest sports and entertainment companies in the United States.• Architected and created data models that handled premium suite revenues documenting millions of dollars for two of the largest sports and entertainment companies in the United States.• Aided the implementation of ETL, data warehousing, and visual reporting for email marketing, financial data, and financial donations for one of the largest universities in the United Sates.• Create data visualization reports for professional sports companies displaying intuitive fan demographics and statistical analysis using Tableau and Power BI.• Assisted in creating an algorithm that calculated marketing opportunities for hotels handling billions of records in Redshift for AWS.• Oversaw all Python code in technical due diligence for consulting branch of SSB.• Handled reports for different tourism groups that displayed top countries and major statistical areas that searched and booked hotels from different areas in the world.• Worked with professional sports teams and higher education to manage their ticketing, merchandise, concessions, and customer databases.

Work with clients that need help and guidance with database management regarding Q&A for CRM and MDM. Also using SSMS and Tableau for reporting purposes and data visualization to give clients a better vision of trends in data.

Worked face to face with students who needed additional tutoring help in mathematics and statistics. Specialized in fundamental math, statistics, calculus, regression analysis, and time series forecasting.

Served as student-athlete liaisons between happenings on campus, in conferences and at national levels.