Juan Carlos Marco Gil
About Juan Carlos Marco Gil
Juan Carlos Marco Gil is a Data Scientist with a background in software engineering and a focus on genomics. He has worked at various companies, including Binartis Genomics and Idrica, and currently holds a position at Keepler Data Tech.
Work at Keepler
Currently, Juan Carlos Marco Gil serves as a Data Scientist at Keepler Data Tech. He has been in this role since 2024, working remotely from Valencia, Valencian Community, Spain. In this position, he applies his expertise in data science to various projects, contributing to the company's focus on data-driven solutions.
Previous Experience at Binartis Genomics
Juan Carlos Marco Gil has extensive experience at Binartis Genomics, where he held multiple roles. He worked as a Software Engineer for a total of 12 months, first for 4 months in 2020 and then for an additional 8 months from 2020 to 2021. Following this, he served as the Research and Development Director for 1 year from 2021 to 2022. His work there focused on integrating software engineering with research and development in genomics.
Experience at Idrica
From 2022 to 2024, Juan Carlos Marco Gil worked as a Data Scientist at Idrica in Valencia, Valencian Community, Spain. During his tenure, he utilized his data science skills to support various initiatives within the company, enhancing data-driven decision-making processes.
Education and Expertise
Juan Carlos Marco Gil studied at the University of Valencia, where he earned a degree in Mathematics (Grado). He possesses a diverse skill set in programming languages, including Python, R, C++, Matlab, PHP, and Javascript. His expertise lies in applying data science tools, particularly in the genomics field, with a focus on predictive and preventive medicine.
Research and Development Interests
Juan Carlos Marco Gil has a strong interest in research, development, and innovation within the field of data science. He actively seeks to enhance his problem-solving skills and improve efficiency in work processes, contributing to advancements in data science applications.