With over six years of work experience in machine learning, distributed systems, and natural language processing, I am passionate about creating innovative solutions that enhance human-computer interaction and improve customer satisfaction. As an Applied Scientist I at Amazon, I work on the Device Invocation team, where I apply cutting-edge edge ML techniques to enable Alexa devices to respond to complex and natural voice commands. I bring a unique perspective and experience as a Fulbright scholar and a bilingual speaker of Spanish and English.In my current role, I leverage my skills in Python, Pandas, SciPy, and Tensorflow to develop and deploy scalable and robust ML models that run on various Alexa devices. I also collaborate with other ML engineers and scientists to conduct experiments, evaluate performance, and optimize results. Previously, I worked as an Engineer at Eightfold, where I developed an NLP model that delivered personalized suggestions based on user browsing experience, using a callable API as backend and a dynamic frontend with React. I also completed an internship at Amazon Alexa, where I worked on a project for using self-supervised learning for speech recognition and wakeword verification for low annotated environments, which was selected for publication. I have a master's degree in computer science from Columbia University a masters degree in Actuarial Science and Statistics from Columbia University, and I have completed multiple certifications and courses in machine learning, deep learning, data structures, and OOP in Java.