B.S. in Data Science and Mathematics at Instituto Tecnológico y de Estudios Superiores de Monterrey
Prev SWE intern at Google.
I'm a passionate B.S. in Data Science student with a keen interest in machine learning and artificial intelligence. My academic journey and professional experiences have equipped me with a robust skill set in data analysis, Python programming, and cutting-edge AI technologies.
As a Software Engineer Intern at Google, I've had the opportunity to work with Large Language Models (LLMs) and develop data processing pipelines that impact over 100 teams. My experience extends to roles such as Data Analyst at DiDi, where I applied A/B testing and time series forecasting to drive data-driven decisions, and Data Science Intern at Summer of Bitcoin, where I worked on clustering and outlier detection in the Lightning Network.
I'm currently serving as the President of the Board of Directors for Algoritmia, the largest computer science club at my school, where I lead a community of over 700 members. This role has honed my leadership and team management skills, complementing my technical abilities.
My diverse experiences across tech giants and startups have given me a unique perspective on how data science and AI can be leveraged to solve real-world problems. I'm always eager to explore new challenges in the field of data science and contribute to innovative projects.
I'm passionate about turning data into actionable insights and developing AI-driven solutions that make a difference. If you're interested in discussing data science, AI, or potential collaborations, I'd love to connect. Feel free to reach out!
My internship project centered on the cutting-edge field of Large Language Models (LLMs), a key area in Google's AI research and development. This specialized project provided me with the opportunity to work at the forefront of AI technology, contributing to advancements that have far-reaching implications across various industries.
Key Project Components:
- Data Processing Pipeline: I developed a robust pipeline for retrieving, processing, and storing relevant data to then prompt an LLM. This involved working with Google's internal tools for multiparallel data processing.
- Prompt Engineering: A significant portion of my work focused on prompt engineering, a crucial aspect of effectively leveraging LLMs. I designed and iterated on various prompt templates, analyzing their effectiveness in generating desired outputs. This process enhanced my understanding of how subtle changes in prompts can significantly impact LLM performance.
- LLM Evaluation and Refinement: I had the privilege of working with Google's latest internal LLM, conducting extensive testing and evaluation. This involved comparing responses from different prompt templates and using embedding vectors to analyze response similarity.
- Data Visualization: To present our findings effectively, I created comprehensive dashboards using SQL and Google's internal visualization tools. These dashboards allowed for easy access to LLM recommendations and performance metrics.
- Documentation and Presentation: Throughout the project, I maintained detailed documentation and ultimately presented my work to team members, gaining valuable feedback for further refinement.
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