Pamela Seida
About Pamela Seida
Pamela Seida is an Application Scientist with extensive experience in cheminformatics and data management. She has worked at Dotmatics since 2015 and has held various roles in notable companies such as BIOVIA and Accelrys, contributing to her expertise in data integration and programming.
Current Role at Dotmatics
Pamela Seida has been serving as an Application Scientist at Dotmatics since 2015. In this role, she focuses on providing scientific support and expertise in the development and management of Discovery databases and electronic notebooks. Her contributions enhance the efficiency of data management and analysis within the organization.
Previous Experience in Application Science
Prior to her current position, Pamela Seida worked at BIOVIA, Dassault Systèmes as an Advisory Field Application Scientist from 2014 to 2015. She also held the same title at Accelrys from 2013 to 2014. Her roles involved advising on application solutions and supporting clients in the implementation of cheminformatics tools.
Educational Background
Pamela Seida studied at Bryn Mawr College, where she earned her PhD from 1989 to 1994. She also completed her undergraduate studies at the same institution, achieving an AB degree from 1984 to 1988. Her education provided a strong foundation for her career in cheminformatics and application science.
Technical Skills and Expertise
Pamela Seida possesses extensive technical skills in UNIX/Linux systems administration and computer programming. She is proficient in multiple programming languages, including PERL, Python, Java, and MATLAB. Her expertise extends to cheminformatics software such as SARgen and SARview, as well as data visualization tools like Spotfire.
Career in Cheminformatics
Pamela Seida has a significant background in cheminformatics, having worked at Accelrys in various roles, including Sr. Cheminformatics Solutions Scientist and Lead Cheminformatics Solutions Scientist from 2010 to 2013. She has experience with computational chemistry tools such as MOE and the Schrodinger Suite, contributing to her proficiency in data integration and analysis.