Adult assessments have evolved to keep pace with the changing nature of adult literacy and learning demands. As the importance of information and communication technologies (ICT) continues to grow, measures of ICT literacy skills, digital reading, and problem solving in technology-rich environments (PSTRE) are increasingly important topics for exploration through computer-based assessment (CBA). The Program for the International Assessment of Adult Competencies (PIAAC) is the first international household survey of adult skills predominantly collected using ICT skills. An issue that PIAAC attempts to provide a clear picture for is the match between supply and demand for employment skills (OECD, 2016, p. 3). There has been increasing interest in exploring the relationship between proficiency levels and subgroups by employment-related variables, such as employment status and skills used at work (e.g., OECD, 2016, p. 102--103; Perry, Wiederhold, & Ackermann-Piek, 2016). However, assessment of skills is merely one step toward a more balanced labor market. Knowing which subgroups performed better is a good starting point, but the processes that gave rise to the final proficiency levels are more informative for providing necessary education. To bridge the gap between supply and demand and provide targeted intervention, it is important to understand which subgroups performed at a lower level and why. Specifically, how did these test takers arrive at a certain wrong answer, and how did subgroups differ in terms of problem-solving strategies? The present study mainly focuses on employment-related variables and the U.S. sample to further identify important factors associated with problem-solving skills. Specifically, three research questions are addressed via exploring the process data from one representative PSTRE item: (1) What features can be extracted from process data discriminating best between subgroups with different employment-related variables?; (2) Clustering test takers based on features extracted from process data, what do test takers in each cluster have in common regarding employment-related variables? And what are the characteristics of the clusters with respect to employment?; and (3) Are test takers' response behaviors consistent across items? What are the employment-related characteristics of the test takers that are consistent, versus those who are inconsistent in their response behaviors? Findings and implications of the study are discussed, shedding light on future research directions.