Career Transitions and Trajectories: A Case Study in Computing

TL;DR

This study analyzes decades of computing research careers using a new dataset and a novel career network model, revealing insights into career paths, organizational influence, and predicting career transitions.

Contribution

It introduces R^3, a versatile model capturing temporal career dynamics, and provides the first comprehensive analysis of long-term computing research careers.

Findings

Identifies key organizations influencing computing careers

Tracks career movements across industry, academia, and government

Develops a predictive model for individual career transitions

Abstract

From artificial intelligence to network security to hardware design, it is well-known that computing research drives many important technological and societal advancements. However, less is known about the long-term career paths of the people behind these innovations. What do their careers reveal about the evolution of computing research? Which institutions were and are the most important in this field, and for what reasons? Can insights into computing career trajectories help predict employer retention? In this paper we analyze several decades of post-PhD computing careers using a large new dataset rich with professional information, and propose a versatile career network model, R^3, that captures temporal career dynamics. With R^3 we track important organizations in computing research history, analyze career movement between industry, academia, and government, and build a powerful predictive model for individual career transitions. Our study, the first of its kind, is a starting point for understanding computing research careers, and may inform employer recruitment and retention mechanisms at a time when the demand for specialized computational expertise far exceeds supply.