Navigating the Intersection of Data Science and Social Work Research: Perspectives from Early to Senior Career Researchers

The growing impact of data science and artificial intelligence across social services has created both opportunities and challenges for social work research. As data science becomes increasingly popular tools for understanding complex social problems, many early-career social work scholars find themselves navigating uncharted territories. The social work profession could better fulfill its mission of addressing social inequities by employing data science that could enhance intervention and policy analysis. What are the applications in different research areas? What are the pathways that early-career researchers have taken thus far to develop these interdisciplinary skills? This roundtable aims to demystify the process of integrating data science into social work research and encourage more social work researchers to develop data science competencies. Panelists will also speak to their specific areas of work, demonstrating how they apply data science to address Grand Challenges for Social Work. In this roundtable, early- and senior-career researchers working at the intersection of social work and data science will share their experiences, challenges, and strategies. Through semi-structured discussion, panelists will explore pivotal experiences that led them to engage with data science and artificial intelligence, including how their backgrounds in quantitative research or other methodological training influenced this transition. A focus will be placed on the process of developing programming skills, with particular attention to the role of training programs (e.g., SICSS, DSSG, workshops, and certificate programs) and the utilization of Large Language Models for coding assistance. Panelists will also address strategies for finding and cultivating interdisciplinary networks and collaborators (e.g., EAAMO), approaches to seeking external funding opportunities in the current environment, and publication strategies for interdisciplinary research spanning social work and data science. The session's key takeaways will include practical guidance for social work researchers interested in applying data science methodologies to their work, strategies for overcoming common barriers to entry in this interdisciplinary space, and approaches for cultivating collaborative relationships with data scientists and computer scientists. These takeaways also provide insights for the development of doctoral training for the next generation of social work scholars equipped with data science literacy and skills. Panelists will share their diverse pathways into this field, highlighting both successes and challenges they have encountered. They will discuss specific training resources that proved valuable, how they navigated publication outlets for work that spans disciplines, and their experiences building networks outside traditional social work circles. The panelists will also address how mentorship has supported their development in this interdisciplinary space as well as their efforts in their labs and professional circles to continuously develop their data science skills while creating meaningful mentoring opportunities for students under their supervision. This roundtable will create space for audience participation to foster community building for collaboration among researchers with similar interests. .