Senior Research Scientist, Information Science – Extended Temporary
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Job Description
Senior Research Scientist developing AI/NLP systems for scientific feasibility assessment at the University of Arizona. Leading reproducible research pipelines, empirical studies, publications, and student collaboration.
Responsibilities:
- Lead the design, implementation, and evaluation of AI/NLP systems for scientific feasibility assessment
- Develop and test integrated pipelines combining literature-based discovery, structured knowledge extraction, simulation, and code-based experimentation
- Run experiments and benchmarking at scale
- Architect and maintain research codebases in Python and related tools
- Implement automated experimentation frameworks, simulation integrations, and evaluation infrastructure
- Ensure reproducible artifacts, logging, and documentation for large-scale empirical studies
- Design and execute systematic empirical analyses of model behavior and experimental outcomes
- Analyze large-scale datasets, intermediate outputs, and logs to diagnose system performance and refine methods
- Design and execute user studies of human scientific feasibility assessment
- Lead and co-author conference and journal papers for top-tier AI/NLP and machine learning venues
- Prepare manuscripts for peer-reviewed publication
- Create technical presentations and present findings at national and international conferences
- Develop research directions, experimental methodology, and project roadmaps
- Assist with technical reports and research documentation
- Drive collaboration, manage relationships, and provide technical guidance to student researchers
- Coordinate with collaborators within and outside the University for joint experiments, data integration, and research dissemination
- Contribute to a collegial, respectful, and collaborative research environment
Requirements:
- Exceptional programming ability, particularly in Python, and strong software engineering practices
- Demonstrated ability to design and maintain substantial research codebases
- Strong knowledge of AI/NLP research methods, including experimental design, evaluation, and benchmarking
- Knowledge of user studies, IRB protocols, human subjects research
- Strong demonstrated ability at hands-on empirical research, including extensive interaction with generating and analyzing large volumes of data, intermediate representations, and experimental outputs
- Familiarity with Linux-based research environments, Git, containerization (e.g. Docker), and reproducible workflows
- Ability to work independently in a research-driven, small-lab setting
- Strong written and verbal communication skills
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