Aaron has a passion for understanding people and helping them reach their maximum potential. His work has contributed to the advancement of Fortune 500 businesses in the domains of talent, culture, organizational wellness and operational effectiveness. He has conducted studies on resilience and overcoming adversity in the workplace as well as how to effectively use use biodata and a wide range of personality traits and characteristics to predict job outcomes such as innovative tendency, job satisfaction, job dedication, organizational citizenship, organizational support, counterproductive work behaviors, burnout, and turnover intentions. His background in applying science and the scientific method in business contexts combined with his experience applying evidence-based insights to achieve target objectives has honed his skills in collecting data fast, deriving insights quickly, and using these evidence-based insights to propose, develop, and implement tools and solutions useful for a wide range of business purposes including HR/People Operations, Business Development, Learning and Development, Change Management, and more.

His knowledge, skills, and abilities in work and organizational psychology allow him to skillfully use data, science, and evidence-based management to make informed business decisions, help organizations and individuals overcome challenges, prevent problems, and promote organizational health using a rigorous data-driven approach. His experience and training as a data scientist allows him to draw practical conclusions from massive datasets while automating processes.

He has a wide range of experience developing and validating psychometric assessments. He has studied individuals, work groups, and entire organizations using both single and repeated measures research designs as well as cross-sectional, experimental, and quasi-experimental research designs. He has conducted systematic reviews of relevant academic literature. His research designs range in complexity from simple t-tests and analysis of variance to machine learning methods and structural equation modeling. He likes to use these skills in his off-time as well. Side-projects that he has worked on apply data, coding, and machine learning to facilitate computer vision and computer-generated art. Other projects involve integrating data, science, and automated technology to optimize his own personal urban hydroponic microgreen grow system. He loves the fact that some of the best results of his efforts with data and science now get to become his lunch.