Research Software Engineer

Position Information Posting date: 07/13/2026 Open Until Filled: Yes Position Number: 1129656 Title: Research Computing Consultant II - Data Science Hiring Range: $87,700 - $115,000 Union: Not a Union Position FLSA Status: Exempt Employment Category: Regular Full Time Scheduled Months per Year: 12 Scheduled Hours per Week: 40 Location: Hanover, NH Remote Work Eligibility: Hybrid Term Position: No Position Purpose The Research Computing Consultant II (RCCII) – Data Science 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. The RCCII applies methodological knowledge across a broad range of research contexts, spanning biomedical and public health sciences through to the social sciences and humanities. This is an early‑career professional role designed for a curious individual with a foundational background and some experience in applied quantitative research support who is ready to develop their expertise within a collaborative, research‑oriented environment. The position reports to the Director of Research Engagement and Data Science and works in close partnership with the Research Cyberinfrastructure and Research Software Engineering teams. Strong programming proficiency in R and Python, effective communication skills, workshop hosting experience, and a genuine enthusiasm for collaborative research are essential to success in this role. Description Dartmouth College seeks a 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 II – Data Science 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. The role is 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. Key Accountabilities Researcher Consultation and Data Science Support (40%) Assists faculty, postdoctoral scholars, and graduate students in assessing analytic needs and identifying appropriate quantitative, statistical, and computational approaches suited to their research questions and disciplinary context. Provides foundational guidance on model selection, validation strategies, and interpretation of analytical results across diverse research domains including biomedicine, public health, social sciences, and the humanities. Guides researchers in evaluating and responsibly adopting AI‑enabled tools, including large language models, to enhance their analytical workflows and research practice. Assists researchers in structuring, cleaning, and summarizing complex datasets to support exploratory analysis, reporting, and communication of findings. Supports implementation of commonly used statistical analyses including regression, mixed‑effects, survival, and multivariate approaches using R and Python. Contributes to the preparation of methods sections, analytical summaries, and data narratives for research manuscripts and proposals. Assists with study design discussions and supports analytical planning related to research proposals. Helps develop text and corpus analysis workflows, including data extraction, preprocessing, and quantitative characterization of textual or qualitative data sources. Assists project teams in developing reproducible workflows using scripting, version control, and workflow management best practices. Troubleshoots routine analytical and methodological challenges, and works closely with senior team members to resolve more complex problems. HPC, Cloud, and AI Workflow Enablement (15%) Assists researchers in effectively using Dartmouth’s high‑performance computing environments and cloud‑based research platforms including job submission, monitoring, and basic troubleshooting. Collaborates with the Research Cyberinfrastructure and Research Software Engineering colleagues to communicate user needs and improve the research computing experience. Helps academic partners evaluate and appropriately use AI‑enabled tools within data science and outreach workflows. Outreach, Education, and Research Computing Adoption (25%) Assists in the design and delivery of introductory workshops and training sessions on applied statistics, data science methods, data visualization, artificial intelligence, reproducible research practices, and HPC usage. Develops accessible documentation, tutorials, and practical learning resources for researchers at all career stages. Provides accessible consultation and training support. Engages departments and interdisciplinary research groups to expand awareness and adoption of research computing and data science services. Translates complex statistical and computational concepts into clear, accessible guidance for audiences with diverse technical backgrounds. Data Management, Visualization, and Research Lifecycle Support (20%) Develops publication‑quality data visualizations using R, Python, or comparable tools, with attention to accessibility and clarity. Supports development of interactive dashboards and reporting tools to help researchers communicate findings effectively (e.g., Shiny, Dash, or comparable platforms). Advises investigators on data organization, storage strategies, and lifecycle management practices appropriate to their discipline and compliance requirements. Provides guidance on responsible and compliant use of data science and AI tools in research contexts. Promotes documentation and reproducibility as foundational practices across research projects. Professional Development and Institutional Contribution Maintains and actively expands knowledge of applied statistical methods, data science methodologies, research computing technologies, and emerging developments in AI as they apply to research practice. Serves as a collegial resource to team members and contributes to a collaborative, service‑oriented environment. Promotes equitable and inclusive access to research computing resources, statistical consultation, and training across campus communities. Additional Responsibilities Demonstrates professionalism and collegiality through actions, interactions, and communications with others appropriate to an environment that is welcoming to all. Performs other duties as assigned. Required Qualifications Education: Master’s degree 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, including at minimum regression and mixed‑effects frameworks. Working knowledge of large language models or AI‑assisted tools and their potential applications in research or data science contexts. Familiarity with high‑performance computing or advanced computational environments. Demonstrated ability to communicate quantitative concepts clearly to audiences with diverse technical backgrounds. Experience developing or delivering technical training, documentation, or instructional materials. Ability to work on campus multiple days per week; this is a hybrid‑eligible role requiring consistent in‑person presence to support the research community. Preferred Qualifications Ph.D. in a quantitative discipline such as biostatistics, statistics, data science, epidemiology, or computational science. Proficiency in SAS or Stata. Strong data visualization skills, including experience developing interactive dashboards (e.g., Shiny, Dash, or comparable platforms). Demonstrated ability to design, evaluate, or advise on AI‑enabled workflows in academic or research settings, including critical assessment of appropriate use and limitations. Working knowledge of Linux‑based systems and workload scheduling tools (e.g., Slurm). Background working with large‑scale, high‑dimensional, or sensitive datasets, including awareness of relevant regulatory and ethical considerations. Exposure to research proposals or grant writing processes, including contribution to methods or computational sections. Active or emerging engagement with scholarly communities through publications, conference presentations, or research working groups. Grounding in research data management best practices and compliance considerations. Equal Opportunity Employer Dartmouth College is an equal opportunity employer under federal law. We prohibit discrimination on the basis of race, color, religion, sex, age, national origin, sexual orientation, gender identity or expression, disability, veteran status, marital status, or any other legally protected status. Applications are welcome from all. Background Check Employment in this position is contingent upon consent to and successful completion of a pre‑employment background check, which may include a criminal background check, reference checks, verification of work history, conduct review, and verification of any required academic credentials, licenses, and/or certifications, with results acceptable to Dartmouth College. A criminal conviction will not automatically disqualify an applicant from employment. Background check information will be used in a confidential, non‑discriminatory manner consistent with state and federal law. Special Instructions to Applicants Dartmouth College has a Tobacco‑Free Policy. Smoking and the use of tobacco‑based products (including smokeless tobacco) are prohibited in all facilities, grounds, vehicles or other areas owned, operated or occupied by Dartmouth College with no exceptions. For details, please see our policy. Contact Department Contact for Recruitment Inquiries: Christian Darabos Contact Email: View email address on click.appcast.io Contact Title: Christian Darabos, Sr. Director, Research Computing and Data (RCD) #J-18808-Ljbffr