Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits. The position requires implementing, deploying, maintaining, evaluating, and benchmarking data science and software ideas and techniques that differentiate Applied Service offerings using semiconductor process data. The person will implement prototype user interfaces which host the advanced algorithms; and deploy, test, analyze, and troubleshoot these applications worldwide at service sites. The position will entail the following: Assist in developing data science software prototypes and interfaces for monitoring semiconductor process tools Develop Python scripts to implement key concepts Deploy and maintain prototypes at service sites Collaborate closely with algorithm developers to characterize the algorithms and benchmark their performance, collecting quantitative data assessing effectiveness Evaluate the effectiveness and accuracy of the algorithms by working closely with process and equipment experts, providing feedback to algorithm developers Translate algorithms from one language to another (e.g., from MATLAB to Python) Knowledge and application of statistical / data science techniques, including both conventional Machine Learning, i.e., decision trees, regression Experience with various Artificial Intelligence Solutions, including Large Language Models, Computer Vision and Generative AI applications. Communicate and train field engineers on how to use prototype applications, including writing documentation and creating audio and video tutorials for use for a variety of user types Be able to interpret data science conclusions and relate to practical process issues