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Laurence Hopkins has a diverse work experience spanning over many years. Laurence most recently worked at Spark New Zealand as an HR Lead, focusing on remuneration, systems, and data. Before that, Laurence held the position of Head of Research and Reward at the Universities and Colleges Employers Association (UCEA) for a decade. At UCEA, their responsibilities included leading research and reward services, conducting large pay benchmarking surveys, and providing guidance to higher education institutions. Prior to UCEA, Laurence worked as a Researcher at The Work Foundation, focusing on work and employment policy in the UK. Laurence also worked as a Senior Analyst at The Change Institute, where they led research and evaluation projects on race and faith issues. Laurence's earlier experience includes working as an Operations Manager at Brand Events, where they oversaw all aspects of exhibition and event production.

Laurence Hopkins has a diverse education history that includes a Bachelor of Arts (Hons) degree in Political Science and Psychology from The University of Auckland, which they obtained between 1999 and 2001. In 2005 to 2006, Laurence attended UCL where they pursued a Master's degree (MSc) in Public Policy. Later, in 2012 to 2013, they enrolled in the Chartered Institute of Personnel and Development and successfully completed an Advanced Certificate in Human Resource Management.

In addition to their formal education, Laurence has also obtained several certifications. In February 2020, Laurence earned a certification in "Correlation and regression in R" from DataCamp. In April 2020, they obtained another certification in "Cluster analysis in R." In June 2020, Laurence accomplished certifications in "Data Scientist with R Track - Statement of Accomplishment," "Supervised Learning in R: Classification," and "Unsupervised Learning in R" from DataCamp.

Overall, Laurence Hopkins has a strong educational background in political science, psychology, public policy, and human resource management, along with certifications in data analysis using R.