Jay Weiler is a highly experienced data scientist with a strong background in machine learning evaluation and analytics. With a Bachelor's degree in Liberal Arts, with a focus on the History of Math and Science, from St. John's College, Jay has consistently demonstrated his analytical aptitude across various industries.

Jay has worked with renowned companies such as Adobe, Nextdoor, Walmart eCommerce, and eBay, showcasing his expertise in analyzing large datasets, developing machine learning models, and driving data-based decision-making. As a Data Scientist at Adobe, Jay leverages his skills to evaluate machine learning algorithms and optimize their performance. Similarly, during his tenure at Nextdoor, he contributed to the development of data-driven products and insights, enhancing the user experience for millions of users.

In addition to his data science roles, Jay has also worked in other technical positions, including as a Data Analytics Engineer at Hoodline and as a Consultant at LatentView Analytics. These experiences have broadened his skillset and allowed him to effectively communicate and align technical insights with business objectives.

Jay's passion for education is evident through his previous roles as a Teacher of Math and Science at the School for Independent Learners. This experience further honed his ability to simplify complex concepts and effectively convey them to others.

Beyond his professional endeavors, Jay has also dedicated his time to volunteer work, including radio production volunteering at the SETI Institute. This involvement reflects his interests in science and technology outreach and his commitment to making a positive impact on society.

Jay Weiler is a skilled data scientist, with expertise in machine learning evaluation, analytics, and product development. His educational background and versatile work experience make him a highly sought-after professional in the field. With a passion for education and a commitment to staying at the forefront of industry trends, Jay continues to contribute significantly to the data science community.