Jason Katz, a seasoned tech professional based in New York City, boasts over five years of extensive experience in AI, data science, and software engineering. In his current role at LinkedIn, he contributes to the Job Marketplace AI team, focusing on developing AI models that power job search and recommendation functionalities.
Beyond his primary role, Katz actively engages in data science freelancing via Upwork, collaborating with nearly 100 diverse clients. His notable projects include constructing an algorithmic stock trading platform, crafting proposals for government contracts, and devising models to forecast patent strength.
Outside of tech, he is a self improvement junkie and recently created an online course on Udemy about productivity and efficiency called Rational Optimization.
From 2015-2020, Jason attended Brown University, where he earned a Bachelor’s degree with dual majors in Statistics and Applied Mathematics, followed by a Master’s in Data Science. While there, he was actively involved in pioneering research, contributing to the development of an auto-ML system under a DARPA grant and the creation of a Julia package for the CAOS algorithm, which aids in classifying diseases and organisms based on DNA sequence analysis. Beyond academia, he was also captain of the Track & Field team and was voted Scholar Athlete of the Year in 2019 as he won the Ivy League championship in Long Jump.
Connect with Jason to learn more.
Listen to the Podcast Here, or Find it Wherever You Get Your Podcasts:
Here are Five Things We Cover:
- Importance of Practical Experience: Jason highlights that hands-on learning and the ability to learn from failure are crucial for success in tech, particularly in fields like data visualization and machine learning.
- Efficiency through Text Replacements: Implementing text replacements for commonly used information such as email and physical addresses can significantly streamline workflows and save time.
- Prioritizing Sleep and Health: Katz emphasizes the critical role of sleep in maintaining productivity, advocating for a structured approach to optimizing sleep quality and overall health to enhance performance in all areas of life.
- Differences in Tech Roles Across Company Sizes: In larger companies, roles in machine learning and data science may be more specialized compared to smaller companies, where individuals may need to manage both realms.
- Time Management Strategies: Using digital platforms like Apple reminders to track tasks and implementing time management tips such as categorizing events are essential for efficient task management, as demonstrated by Jason’s personal and professional experience.
Here are Three Actionable Takeaways From This Episode
- Start with the Basics: If you’re interested in breaking into machine learning, focus on building a solid foundation in statistics, probability, and mathematics. Understanding these core concepts will provide a strong starting point for learning more advanced machine learning techniques.
- Embrace Continual Learning: Be open to learning and adapting to changes in the industry. Technology and techniques in machine learning are continually evolving, so staying informed about the latest developments and being open to new ideas and approaches will help you stay competitive in the field.
- Seek Hands-On Experience: Don’t just focus on theoretical knowledge. Seek opportunities to apply your learning in practical, real-world situations. Projects, internships, and collaborative learning environments can provide valuable hands-on experience and help solidify your understanding of machine learning concepts.