▪ Early hire for financial services startup using predictive and AI modeling for creditworthiness reporting focusing on the growing problem of aging student loans. ▪ Served as Subject Matter Expert in Data Science and analytics; developing core capabilities using clustering, ensemble machine learning models and time-to-event analysis. ▪ Core technologies selected and deployed included Python libraries, scikit-learn and Tensorflow.▪ Integrated Machine Learning algorithms into risk assessment process- increasing prediction accuracy by 40%.▪ Used advanced analytics and data science to optimize credit scoring models leading to an increase in approvals at a consistent level of risk. ▪ Led and managed a team of 14 while deploying cloud solutions for big data. Team enabled real time risk monitoring and decision making, improved operational efficiency and allowed for strategic data based business decisions as well as actionable customer insights in real time.

▪ Taught classes in Analytics covering probability, statistics, data exploration, visualization, and predictive analysis. ▪ Member of the faculty of the Master of Professional Studies in Analytics Program.

▪ Led 6 month long cohorts in data science and data analytics curriculum, ▪ Taught Python, SQL, parametric and non-parametric ML models including regression, classification, clustering, decision trees, NLP, Random Forest, and Neural Network Models.

▪ Conceived, Designed, and Developed an Independent mobile game for iOS release.▪ Built in-app purchase and reward engines.▪ Designed all gameplay features and collaborated with tester to perform all A/B testing before release.▪ Designed and built AI-driven recommender systems for in-app purchases driven by the player's style of play.

▪ Developed predictive models for deliverable insights to finance and insurance stakeholders. ▪ Developed models, tested, and put models into production. ▪ Models were developed using survival analysis, classification methods, and neural networks. ▪ Gathered data using SQL and modeled with R before porting to Python for the firm.

▪ Created predictive models for various IT use cases including conducting clinical analysis, analyzing health outcomes and forecasting infrastructure traffic and failures using time-to-event and survival analysis.▪ Automated data pipeline to reduce reporting and analysis preparation times from weeks to hours. ▪ Leveraged mass structured data from a variety of sources including clinical and marketing data warehouses▪ Performed analyses using both R and Python. Communicated results and reports using Tableau.