Research Associate in Adolescent Digital Wellbeing

About the role

The Kaleidoscope project is a year-long longitudinal study of approximately 300 adolescents & families, collecting multi-platform behavioural trace data across gaming platforms alongside ecological momentary assessment, qualitative measures, and randomised controlled trials of family-facing wellbeing interventions. Working in close collaboration with the PI, you will take joint ownership of the project's technical infrastructure and quantitative analyses, while also playing an active role in participant recruitment, onboarding, and retention.

What you would be doing

You will take ownership of the quantitative and technical infrastructure of the Kaleidoscope project, working in close collaboration with the PI and a Research Assistant who leads participant recruitment and day-to-day engagement. Based on your expertise & interests, you will lead elements of the data collection pipeline and analytical strategy. This includes contributing to and maintaining data collection systems (REST APIs, SQL databases, mobile-based ESM tools), ensuring data pipelines are robust, well-documented, and version-controlled, and leading advanced quantitative analyses of intensive longitudinal and behavioural trace data using causal inference and multilevel modelling approaches. You will contribute to the design and analysis of randomised controlled trials testing family-facing interventions emerging from the study's qualitative findings, and co-author reproducible open science manuscripts including writing & maintaining analysis code in R or Python.

What we are looking for

• A PhD in a relevant discipline (e.g., computational social science, psychology, human-computer interaction, data science)
• A strong publication track record in relevant venues, commensurate with career stage
• Demonstrated use and commitment to open science embedded throughout the research process (e.g., registered reports, open data, and reproducible workflows)
• Experience analysing intensive longitudinal data (e.g., ESM/EMA), including multilevel modelling approaches
• Experience conducting research with human participants in a longitudinal context, including recruitment, onboarding, and retention
• Strong programming skills in R and/or Python, including writing clean, documented, version-controlled code suitable for collaborative and reproducible research
• Experience with or strong knowledge of causal inference methods (e.g., DAGs, potential outcomes framework)
• Familiarity with data infrastructure at a practical working level (e.g., REST APIs, SQL databases)
• Experience with intervention design and/or randomised controlled trials