Research Computing Consultant II – Data Science

Research Computing Consultant II – Data Science

The Research Computing Consultant II – Data Science (RCCII) is a researcher‑facing professional who partners with faculty, postdoctoral scholars, and graduate students to support data‑intensive research across disciplines. This role applies expertise in applied statistics, artificial intelligence, and data science to help the campus research community design rigorous analytical workflows, develop publication‑quality visualizations, and build reproducible computational practices.

• Assist faculty, postdoctoral scholars, and graduate students in assessing analytic needs and identifying quantitative, statistical, and computational approaches suited to their research questions.
• Provide foundational guidance on model selection, validation strategies, and interpretation of analytical results across a range of domains, including biomedicine, public health, social sciences, and humanities.
• Guide researchers in evaluating and responsibly adopting AI‑enabled tools, such as large language models, to enhance analytical workflows and research practice.
• Assist researchers in structuring, cleaning, and summarizing complex datasets, and support implementation of common statistical analyses (regression, mixed‑effects, survival, multivariate) using R and Python.
• Contribute to the preparation of methods sections, analytical summaries, and data narratives for manuscripts and proposals.
• Assist with study design discussions and support analytical planning related to research proposals.
• Help develop text and corpus analysis workflows, including data extraction, preprocessing, and quantitative characterization of textual or qualitative data sources.
• Assist project teams in developing reproducible workflows using scripting, version control, and workflow management best practices.
• Troubleshoot routine analytical and methodological challenges; work closely with senior team members to resolve more complex problems.
• Support researchers in using Dartmouth’s HPC and cloud platforms (basic job submission, monitoring, troubleshooting).
• Collaborate with Research Cyberinfrastructure and Research Software Engineering teams to communicate user needs and improve the research computing experience.
• Assist academic partners in evaluating AI‑enabled tools within data science and outreach workflows.
• Design and deliver introductory workshops and training sessions on applied statistics, data science methods, data visualization, artificial intelligence, reproducible research practices, and HPC usage.
• Develop accessible documentation, tutorials, and learning resources for researchers at all career stages.
• Engage departments and interdisciplinary research groups to expand awareness and adoption of research computing and data science services.
• Translate complex statistical and computational concepts into clear, accessible guidance for audiences with diverse technical backgrounds.
• Develop publication‑quality data visualizations and interactive dashboards using R, Python, or comparable platforms.
• Advise investigators on data organization, storage strategies, and lifecycle management appropriate to discipline and compliance requirements.
• Promote documentation and reproducibility as foundational practices across research projects.
• Maintain and expand knowledge of applied statistical methods, data science methodologies, research computing technologies, and emerging AI developments.
• Serve as a collegial resource to team members and contribute to a collaborative, service‑oriented environment.
• Promote equitable and inclusive access to research computing resources, statistical consultation, and training across campus communities.

• Master’s degree in biostatistics, statistics, data science, epidemiology, computer science, computational science, or a related quantitative discipline.
• Minimum of one year of experience in applied data science, applied statistics, research computing, or computational research support.
• Experience supporting or participating in data‑intensive research projects.
• Strong proficiency in both R and Python.
• Demonstrated experience with applied statistical modeling,…