Research Computing Consultant III – Data Science & HPC

Position Purpose

The Research Computing Consultant III (RCCIII) – Data Science & HPC is a senior researcher-facing professional who partners with faculty, postdoctoral scholars, and graduate students to support data-intensive research across disciplines. This role designs, implements, and optimizes data science and computational workflows, including statistical analysis, machine learning, AI-enabled tools, and high-performance computing, while reducing the complexity of advanced technologies through consultation, training, and hands-on technical facilitation across the research lifecycle.
Operating with a high degree of independence, the RCCIII serves as an expert data scientist with advanced knowledge of applied statistical methodologies and significant experience designing, building, and supporting scalable computational pipelines in high-performance computing environments. In this capacity, the position serves as an advanced subject matter expert in applied data science and research computing, promoting scalable, reproducible, and effective research practices. The role reports to the Director of Research Engagement and Data Science and collaborates closely with research computing infrastructure teams and campus partners to enhance the overall researcher experience.

Description

Dartmouth College seeks a senior research computing professional to serve as a computational thought partner to faculty and research teams across disciplines. This role sits at the intersection of applied data science, advanced computing, and collaborative research strategy.
The Research Computing Consultant III – Data Science & HPC advances Dartmouth’s research mission by enabling scalable, reproducible, and innovative computational practices. This position partners directly with faculty, postdoctoral scholars, and graduate students to shape analytical approaches, optimize computational workflows, and expand adoption of advanced research technologies.
This is a senior-level role within the Research Engagement and Data Science team, reporting to the Director of Research Engagement and Data Science and working in close collaboration with research computing infrastructure colleagues and campus partners.

Required Qualifications - Education and Yrs Exp

Masters plus 3-5 years' experience or equivalent combination of education and experience

Required Qualifications - Skills, Knowledge and Abilities

• Master’s degree in data science, statistics, computer science, computational science, or a related quantitative discipline.
• Minimum of five years of experience in applied data science, research computing, or computational research support.
• Demonstrated experience working directly with researchers to support data-intensive projects.
• Strong proficiency in at least one primary data science programming language such as R or Python.
• Demonstrated experience designing, building, and supporting scalable data science or statistical pipelines in high-performance computing or advanced computational environments.
• Demonstrated ability to communicate complex analytical concepts to audiences with diverse technical backgrounds.
• Experience developing or delivering technical training, workshops, or instructional materials.

Preferred Qualifications

• Ph.D. in a quantitative discipline.
• Experience supporting artificial intelligence or machine learning workflows in academic research settings.
• Familiarity with Linux-based systems and workload scheduling tools.
• Experience working with large-scale, high-dimensional, or sensitive datasets.
• Experience contributing to interdisciplinary research proposals or sponsored research initiatives.
• Familiarity with research data management best practices and compliance considerations.

Researcher Consultation and Applied Data Science Support

• Partners with faculty, postdoctoral scholars, and graduate students to assess analytic and computational needs and recommend appropriate data science approaches.
• Designs and supports implementation of statistical analyses, machine learning models, and data processing pipelines using languages such as R, Python, or comparable tools.
• Assists researchers in developing reproducible workflows using scripting, version control, and workflow management practices.
• Provides guidance on model selection, validation strategies, and interpretation of analytical results.
• Troubleshoots complex analytical and computational challenges across diverse research domains.
• Contributes technical expertise to research proposals and sponsored project planning as appropriate

Percentage Of Time: 35

HPC, Cloud, and AI Workflow Enablement

• Guides researchers in effective use of high-performance computing environments and cloud-based research platforms.
• Advises on scaling analyses for large, high-dimensional, or computationally intensive datasets.
• Supports integration of artificial intelligence and machine learning tools into research workflows.
• Assists with configuration of computational environments to promote efficiency and reproducibility.
• Collaborates with research computing infrastructure teams to communicate researcher needs and improve usability of systems and services.

Percentage Of Time: 25

Outreach, Education, and Research Computing Adoption

• Designs and delivers workshops, training sessions, and onboarding programs related to data science, AI tools, reproducible research, and advanced computing practices.
• Hosts regular office hours and consultation sessions to provide accessible, researcher-centered support.
• Develops documentation, tutorials, and practical learning resources.
• Engages departments and interdisciplinary research groups to expand awareness and adoption of research computing services.
• Translates complex technical concepts into clear guidance for diverse audiences.

Percentage Of Time: 20

Data Management, Visualization, and Research Lifecycle Support

• Advises researchers on data organization, storage strategies, and lifecycle management practices.
• Supports development of visualization and reporting approaches.
• Provides guidance on responsible and compliant use of data science and AI tools.
• Promotes documentation and reproducibility across research projects.

Percentage Of Time: 10

Professional Development and Institutional Contribution

• Maintains advanced knowledge of emerging data science methodologies and research computing technologies.
• Serves as a technical resource to colleagues.
• Contributes to a collaborative, service-oriented team environment.
• Promotes inclusive and equitable access to research computing resources and training.

Percentage Of Time: 10