AI & Research Data Science Specialist

Description

Core Responsibilities

• Developing services to support the CMU research community related to areas including computational programming, data modeling, data pipelines, data representation, data preservation, and statistical analysis. This may include partnerships on sponsored research projects.
• Offering training, advising, and support to the CMU Library community on the utilization of AI, natural language processing, computer vision, or machine learning tools and related methods to advance library practices.
• Working in collaboration with others across the library, offer extended consulting services for researchers on data use/reuse, workflows, reproducibility, and the utilization of computational tools and methods.
• Partnering with liaison librarians and staff to assess and support the data needs of specific departments and subject areas.
• Acquiring and maintaining expertise in trends with information synthesis, automated science, and data science practices.
• Collaborating on sophisticated computational projects with the ability to communicate processes to non-experts.

Qualifications

• 3-5 Years of Data Analysis and Design Experience
• 3-5 Years Research Computing Experience
• Experience conducting academic research
• Experience with data science tools (e.g., Jupyter Notebooks, GitHub, Docker) and computational methods (e.g., machine learning, computer visioning, natural language processing.)
• Experience using or teaching programming languages commonly used in research such as R, Python, javascript, MatLab, or C/C++.
• Detailed knowledge of, or direct experience in, working with both structured and unstructured data
• Direct experience with the information technologies, standards, and practices prevalent in data curation and/or research reproducibility.
• Direct experience in conducting research and in managing, sharing, and curating research data, and developing the related metadata.
• Experience working with data repositories
• A strong understanding of research reproducibility and relevant tools, standards, schema, and related practices
• Ability to communicate to both experts and non-experts.
• Excellent written and oral communication skills and a demonstrated ability to present and share ideas clearly and effectively to diverse audiences.