Cindy Espitia, Ph.D.
About Cindy Espitia, Ph.D.
Cindy Espitia, Ph.D., is a Scientist II in Data Standardization at BenchSci, where she applies advanced biomedical artificial intelligence to enhance pharmaceutical drug discovery. With over seven years of experience in molecular biology, genetics, and biochemistry, she has a strong academic background and has worked in both academia and industry.
Current Role at BenchSci
Cindy Espitia, Ph.D., currently serves as Scientist II in Data Standardization at BenchSci. She has been in this position since 2024, working remotely from Toronto, Ontario, Canada. In her role, she contributes to the acceleration of pharmaceutical drug discoveries through the application of advanced biomedical artificial intelligence. Prior to her current position, she worked as Scientist I in the same department from 2023 to 2024.
Previous Experience in Academia
Cindy Espitia has a substantial academic background, having worked at Universidad Industrial de Santander as a Researcher and Teaching Assistant from 2010 to 2012. She also held the position of Doctoral Researcher at the University of Geneva from 2014 to 2018. During her time at the University of Geneva, she was involved in research as a Master Researcher in Biochemistry from 2012 to 2014. This experience has equipped her with a strong foundation in research methodologies.
Educational Background
Cindy Espitia earned her Bachelor's degree in Biology from Universidad Industrial de Santander, where she studied from 2005 to 2012. She then pursued a Master's degree in Biochemistry and Molecular Biology at the University of Geneva from 2012 to 2014. Following her Master's, she completed her Doctor of Philosophy (PhD) in Biology with a focus on Human/Medical Genetics at the University of Geneva from 2014 to 2018. Additionally, she obtained a specialization in Genomic Data Science from The Johns Hopkins University from 2020 to 2021.
Expertise in Molecular Biology and Genetics
Cindy Espitia possesses over seven years of expertise in molecular biology, genetics, and biochemistry. Her interdisciplinary background allows her to navigate effectively between academia and industry, enhancing her contributions to scientific research and development. This expertise supports her current work in data standardization and the application of artificial intelligence in drug discovery.