Jason Lee Email and Phone Number

Sports enthusiast and Machine Learning advocate. Build, evaluate, and deploy various algorithms to make the most accurate predictions possible giving us an edge in the sports investment market.◦ Leadership: Recruited, organized, and led a high-performing team of data scientists to develop and maintain predictive models and fostered a collaborative and inclusive team culture that prioritized communication, innovation, and continuous learning.◦ ML Engineering: Built and deployed predictive betting models for NFL, CFB, NBA, CBB, and NHL that have been able to beat the market.◦ Automation: Created automated data collection, cleaning, and storage pipelines to work in conjunction with proprietary modeling to streamline our sports betting operations.

◦ ML Ops: End-to-End development of data science models for predicting team level and player level events in NFL and CFB, resulting in an increase in profit margins.◦ Data Engineering: Contributed to the development of our data infrastructure by identifying opportunities to improve data validation and quality assurance procedures, and implementing solutions to address these issues.◦ Production Monitoring: Evaluated and iterated on existing data science models to continuously improve their accuracy and reliability.◦ Product Development: Conducted research and analysis to inform the creation of new player prop betting markets and worked closely with partners to ensure that new markets were implemented accurately and efficiently

◦ ChatBots: Developed and deployed chatbots for government agencies to improve customer service and support.◦ Bot Development: Built and deployed Bots to collect and analyze online social media posts relating to specific key topics providing valuable insights to guide the training of sales and customer service reps.◦ Computer Vision: Built object recognition model to determine whether government documents had wet,electronic, or no signatures saving hundreds of hours of manual labor for the VA.

ML Engineering: Trained and maintained lead scoring and agent scoring algorithms to prioritize incoming leads and ensuring the optimal sales representative was paired with each lead.Collection Strategy: Built Markov Chain Monte Carlo simulation to determine which action most improves our likelihood of recouping delinquent payments to decrease risk in our loan portfolio.Text Mining: Built web scrapers to collect and analyze online company/competitor reviews providing valuable insights to guide the training of sales reps.

ML Engineering: Created clustering, segmentation, and propensity-to-buy models with machine learning algorithms to target clients with messages tailored to their specific needs.Consult: Subject matter expert for marketing campaigns providing senior leaders with the information required to make appropriate business decisions.Data Mining: Leveraged data from various tools to provide a deep analysis of our marketing efforts to dictate future initiatives.Dashboards: Developed dynamic dashboards connecting disparate data sources in a single place for senior and executive teams to easily track KPIs.

◦ Predictive Analytics: Achieved industry standard score improvements in 1/3 industry average time through relevant data utilization and applied statistics - simulation, segmentation, regression, and classification models◦ Text Mining: Data mined over 10 years of ACT/SAT test questions and answers performing sentiment analysis and other NLP methods finding several trends that helped shape our curriculum.◦ Database Systems: Designed, programmed, and deployed relational database to track thousands of data points per student to supplement human instruction and improve scores. Automated KPI reporting and provided ad-hoc reports to access current standings.

Assessed risk/reward for investments.Research: Researched specific stocks along with market and macroeconomic conditions to determine value.Forecast: Developed models that account for diverse variables to project individual company’s stock prices. Tracked portfolio to determine optimal time to buy and sell.Advise: Presented concise reports to upper management. Recommended buy or sell, long or short positions based on the metrics and current market conditions.