Bryan Lanier
About Bryan Lanier
Bryan Lanier is a Data Engineer at RNWBL, where he has worked since 2023. He has a diverse background in data science and analytics, with previous roles at Quantum Data Technologies and EOG Resources, and holds degrees in Actuarial Science and Business Analytics from The University of Texas at Austin.
Work at RNWBL
Bryan Lanier has been employed as a Data Engineer at RNWBL since 2023. In this role, he works on-site and focuses on data engineering tasks that support the organization's operations. His position at RNWBL marks a significant step in his career, allowing him to apply his extensive background in data science and analytics.
Previous Employment History
Before joining RNWBL, Bryan Lanier worked at Quantum Data Technologies as a Data Scientist from 2022 to 2023. He also served as a Data Scientist II at EOG Resources from 2018 to 2021. His career began in various roles, including a Social Coordinator at College Houses in 2016 and a Floor Manager at Kroger from 2010 to 2013. He has also held positions in analytics and data science at BKO Services, Inc and completed an internship at EOG Resources.
Education and Expertise
Bryan Lanier earned a Bachelor of Applied Science in Actuarial Science from The University of Texas at Austin, completing his studies from 2014 to 2017. He furthered his education by obtaining a Master of Science in Business Analytics from the Texas McCombs School of Business between 2017 and 2018. He also holds a Bachelor's degree in Business Administration and Management from the University of Texas at Tyler, which he completed in 2014.
Technical Skills
Bryan possesses advanced skills in mySQL, where he is recognized for his expertise. He has extensive experience with Python libraries, including Pandas, Numpy, ScikitLearn, TensorFlow/Keras, and Dash/Plotly. His technical proficiency supports his roles in data science and engineering, enabling him to effectively analyze and manage data.
Professional Philosophy
Bryan emphasizes the importance of calm and objective thinking in problem-solving. He believes that while having a vision is straightforward, the execution of that vision requires significant effort. This philosophy guides his approach to his work in data engineering and analytics.