Daniel Gribel

I am a software engineer and data scientist with expertise in machine learning, data engineering, and quantitative analysis. Holding a PhD in optimization and machine learning from the Computer Science Department at PUC-Rio, I have a solid understanding of probabilistic models and algorithms for pattern recognition. My experience with industry projects has equipped me with the ability to manage large-scale datasets and develop effective solutions for pattern recognition and anomaly detection.

Throughout my career, I have worked on various projects involving software development with different programming languages and machine learning platforms. I am proficient in Python, R, Julia, and C++, among others, and have hands-on experience with data visualization tools. My theoretical background in machine learning, probability theory, and time series analysis allows me to approach complex problems with a deep understanding of the underlying principles.

I am a collaborative team player with strong communication skills, having successfully worked with interdisciplinary teams from diverse backgrounds and countries. I am committed to ethical standards and open to learn new programming paradigms, languages, and frameworks.