Senior Research Scientist

University of Colorado•Aurora, CO

3d•Hybrid

About The Position

The Colorado School of Public Health, Department of Biostatistics and Informatics has an opening for a full-time Senior Research Scientist. The Sr. Research Scientist will lead and support advanced data science, machine learning, and artificial intelligence research in clinical and population health domains. The position emphasizes development of end-to-end analytical pipelines, AI-enabled research tools, and scalable data platforms to support multi-site studies, clinical research programs, and translational science initiatives. The incumbent will collaborate closely with faculty investigators, clinicians, epidemiologists, and informatics teams to design, implement, and evaluate advanced computational methods, including machine learning, natural language processing, and large language model (LLM)–based systems. The role includes independent scientific contribution, methodological leadership, and mentorship of research staff and trainees.

Requirements

Master’s degree in Data Science, Computer Science, Biostatistics, Informatics, or a related quantitative field,
Demonstrated experience applying machine learning, AI, or advanced analytics in applied research settings.
Programming experience in Python and/or R; working knowledge of SQL.
Experience developing reproducible data pipelines and analytical workflows.
Evidence of scientific productivity and meaningful contributions to research projects.
Applicants must meet minimum qualifications at the time of hire.

Nice To Haves

Experience in clinical, biomedical, or population health research environments.
Demonstrated expertise with modern ML/AI frameworks (e.g., PyTorch), NLP, and/or large language models.
Experience building AI-enabled research tools such as RAG systems, agent-based workflows, or clinical decision-support applications.
Familiarity with cloud platforms (e.g., AWS, GCP), data warehousing solutions, and high-performance computing.
Experience developing interactive analytics applications (e.g., R Shiny).
Prior mentorship of analysts or trainees.
Record of peer-reviewed publications, research software contributions, or publicly released datasets.

Responsibilities

Lead the design and implementation of advanced research analytics using machine learning, AI, and statistical modeling for clinical, epidemiologic, and multi-omic studies.
Develop and optimize end-to-end data pipelines (ETL/ELT) for multi-source clinical, survey, and high-dimensional biomedical data.
Apply modern ML/AI approaches including supervised learning, deep learning, NLP, and LLM-based systems (e.g., RAG, agentic workflows) to research questions.
Conduct model evaluation, interpretability analyses (e.g., bias assessment, explainability), and validation to ensure scientific rigor and responsible AI use.
Contribute substantively to study design, analysis plans, and dissemination of research findings (manuscripts, abstracts, technical reports, and grant materials).
Architect and maintain scalable research software systems, including AI-enabled research assistants, data exploration tools, and reproducible analysis environments.
Develop interactive data visualization and analytics applications (e.g., R Shiny or equivalent) to support investigators and stakeholders.
Leverage high-performance and distributed computing environments to optimize computational efficiency for large-scale analyses.
Implement software engineering best practices including version control, containerization, CI/CD pipelines, and documentation.
Collaborate with interdisciplinary teams of faculty, clinicians, data scientists, and research staff across departments and institutions.
Provide technical mentorship and guidance to junior analysts, trainees, and research assistants.
Serve as a technical and methodological resource for grant-funded research projects and institutional initiatives.
Ensure data security, privacy, and regulatory compliance (e.g., IRB, HIPAA as applicable).
Follow university, sponsor, and departmental policies for responsible conduct of research.