Responsible for overseeing AI strategy, governance, development, and implementation for the entire University of Miami enterprise which includes the academic institution as well as the UHealth healthcare system.

Head of Data Science overseeing advanced research and development of AI based solutions for UKG's entire product portfolio.• Thought leader, owner, and principal researcher for data science collaborating with our partners in product and engineering to develop a robust backlog of research projects and a detailed multi-year research roadmap. • Face of Data Science by participating in customer presentations (helping earn and retain over $50million in ARR), giving internal and external webinars, participating on a panel of experts and as featured speaker at industry events all with the goal of enhancing our messaging around our AI solutions to solidify UKG as leaders in AI in our space. • Trusted resource in identifying strategic opportunities to leverage AI/ML for the growth and expansion of UKG’s business. This includes corporate development, acting as the go-to authority for evaluating potential partnership or acquisition opportunities based on companies' AI/ML capabilities. • Core competencies include Forecasting, AI-powered resource scheduling, Employee Analytics and Insights, Recommendation Systems, Natural Language Understanding, Document AI, and Generative AI. .

Head of Data Science for the direct-to-consumer portion of True Fit. True Marketplace, our first foray into direct-to-consumer e-commerce, is an affiliate marketing platform that combines products and brands across our cooperative of retail partners.• Designed the complete data infrastructure including style recommendations, product tagging and categorization, affiliate link generation, and reporting. • Built style recommendations API from the ground up using Python’s Flask-RESTful framework so that recommendations are fully integrated into the UX. Underlying model is a brand-brand collaborative filtering model with a content based machine learning model to further sort products within brand groupings.• Supported additional direct to consumer initiatives outside of the marketplace, including monetizing and marketing to True Fit across various display media platforms. • Oversaw a cross functional team of data engineers, front end developers and business analysts in the development of the marketplace experience.

Lead of Data Science and Analytics for the Consumer Engagement Initiative at True Fit.

• Lead of the Fit Innovation Group tasked with maintaining and improving on True Fit’s core and most important technology, the size recommendation system, known internally as the Fit Engine. • Built a prototype algorithm that uses catalog data and individual size strings to map a product’s sizes to a common size scale. • Wrote multiple versions of a web crawler and scraping to collect and organize a research set of 6 million product images from our retail partners.• Created proof of concept algorithms using product images to automate the assignment of product attributes such as shape, length.• Designed the first ML based automated product categorization system which is a tiered classifier that assigns a given product to a hierarchy using text and catalog data. Built the offline learning component that is still used in our production system to generate models.• Architect of the initial iteration of our discovery service (personalized product recommendations) and author of the PySpark model trainers used in version one of our production system to create models at scale. Underlying algorithm uses matrix completion along with a machine learning layer for scoring user-product pairs.

Cleaned and structured large, messy, and inconsistent data sets consisting of ~350 million job postings and ~30 million resumes. • Clustered occupations, skills, and other job market entities into more manageable groups and to normalize data elements. • Used Cosine Similarity to determine similarity between these job market entities for research and product use cases. • Modeled missing data elements, such as salary or experience required, using regression methods (statistical methods when appropriate, basic machine learning for performance at scale, and ensemble methods for improved accuracy).Manager of the Analytics Product Development group, a project based group that focused on productizing data and analytics for internal and external use. Led development on multiple projects including the following. • Career Pathways Application: dynamic recommendations engine for career guidance using Python and Neo4J. • ResumeDB: initial prototype database to manage the corpus of 30 million resumes built using Python's xml.etree to traverse the DOM tree of parsed resume xml documents to scrub PII and convert to flat files for import into a production SQL database and development MongoDB environment. • Similarity scoring web service: internal tool for use in research and taxonomy development using Python's Flask framework and hosted on AWS. Led the Data Science group at BGT with the expressed goal of advancing job market analytics in an automated and scalable way. • Managed direct reports, and advised and cooperated with split members of the team. • Migrated the team to improved version control software (GitHub), better communication tools (Slack), and more available and scalable infrastructure (AWS).

First full time hire, trial by fire as the sole Data Scientist and the second member of the engineering team.• Built a browser extension, and a data collection agent in the AWS cloud, to be included into a partner plugin to collect browsing data from users.• Developed a learning model that provides cookie-less tracking of users across sessions with +90% accuracy.• Used machine learning techniques for audience segmentation and website categorization from an existing clickstream data set.• Developed rules for initial bids and bid adjustments for a prototype RTB bidding engine.• Built an internal and client facing analytics web service to report on performance of ad placements.

• Taught over 500 students over the course of five years in classes ranging from Quantitative Reasoning and Math Skills up through each course in the calculussequence. • Effectively explained both basic and complex mathematical concepts to studentsin tangible and relatable terms.