Mark O'grady
About Mark O'grady
Mark O'Grady is a Data Scientist with a strong academic background in Actuarial Science and Theoretical and Mathematical Physics from University College Dublin. He has professional experience in Python, R, and SAS, and has worked in various roles including as a Maths Tutor and an Actuarial Intern.
Work at Push Operations
Mark O'Grady has been employed as a Data Scientist at Push Operations since 2019. In this role, he utilizes his expertise in data analysis and programming to support the company's operational objectives. His work involves leveraging data to drive insights and improve decision-making processes within the organization. Mark's contributions have been integral to the development of data-driven strategies at Push Operations.
Education and Expertise
Mark O'Grady studied at University College Dublin, where he earned a Bachelor's degree in Theoretical and Mathematical Physics from 2012 to 2016. He furthered his education by obtaining a Master of Science in Actuarial Science from 2016 to 2017. His academic background provides a strong foundation in quantitative analysis, which he applies in his current role as a Data Scientist.
Professional Experience
Prior to his current position, Mark O'Grady gained valuable experience in various roles. He worked as a Regulatory Trainee at KPMG Ireland for four months in 2018, where he was exposed to regulatory frameworks. Additionally, he served as an Actuarial Intern at the Department of Social Protection for nine months from 2017 to 2018. Mark also worked as a self-employed Maths Tutor in County Dublin from 2018 to 2019, where he provided personalized instruction in mathematics.
Technical Skills
Mark O'Grady possesses strong technical skills in programming and data analysis. He is proficient in writing scripts using Python, R, and SAS, which are essential tools for data manipulation and statistical analysis. His participation in Kaggle competitions showcases his commitment to continuous learning and application of data science techniques. Additionally, he shares his projects and code on GitHub, contributing to the data science community.