Professor of Practice in Data

Tulane University•New Orleans, LA

About The Position

The Connolly Alexander Institute for Data Science (CAIDS) at Tulane University seeks a full-time, non-tenure-track Professor of Practice in Data to teach introductory and multidisciplinary courses in data literacy, visualization, presentation, management, and/or analysis. The position is eligible for promotion to Senior Professor of Practice. Initial appointments are renewed every three years, with later appointments of up to five or seven years. Renewal is based on evaluation of teaching, service, and research. Professors of Practice may be considered for promotion after at least three years. The position carries a 2/2 teaching load (two courses per semester), allowing time for service. The professor will teach DATA 1010: Introduction to Data, DATA 2030: Data Visualization, and at least one multidisciplinary undergraduate course in their area of interest or expertise. CAIDS is particularly interested in candidates who can add courses in areas such as non-standard data types, survey and polling design and analysis, biostatistics, bioinformatics, clinical trial or experimental data, and AI literacy. CAIDS courses draw on multidisciplinary perspectives and are generally open to students of all backgrounds and abilities without prerequisites. Alongside teaching, the professor will develop and facilitate CAIDS programs and services that increase faculty and student participation in data-related research while supporting the institute’s inclusive mission. Service may include coordinating internships and research-assistant programs, facilitating collaborative grant applications, providing consulting, organizing research programming, and supporting interdisciplinary research groups. The role also supports data visualization co-curricular activities with Howard-Tilton Memorial Library, including competitions, exhibits, poster sessions, and workshops. The professor will help shape CAIDS priorities and manage its programs and initiatives. Conducting research is not required, although data-related research or scholarship on data science teaching and learning may be incorporated into the workload.

Requirements

A Ph.D. in a data-related field and teaching experience as an instructor or experience teaching a data-focused course; or a master’s degree in a data-related field and at least five years of combined experience teaching in post-secondary or secondary education and/or working in industry or government in a data literacy, data analysis, or data management position.
Demonstrated ability to teach in post-secondary institutions, particularly students with minimal background in data, statistics, or computer science.
Ability to initiate and manage programming and initiatives with minimal supervision.
Ability to teach Introduction to Data and Data Visualization.
Ability to develop practical and creative post-secondary programming and services that promote faculty and student engagement in data-related research.
Experience developing data visualizations, including graphs, charts, and interactive tools such as dashboards, using applications such as Excel, PowerBI, Flourish, Tableau, or R.
Commitment to continuous improvement in pedagogy and data science.
Demonstrated commitment to engaging diverse populations in data literacy.
Ability to interact effectively with diverse groups, including students from varied backgrounds, university leadership, staff, and faculty from different disciplines.
Excellent written and oral communication skills.

Nice To Haves

Experience teaching data-focused post-secondary courses as the instructor of record.
Training or education in teaching or pedagogy.
Experience developing or managing post-secondary programming such as workshops, internships, events, and research services.
Ability to provide data, statistical, computer science, or applied AI consulting services to faculty.
Ability to develop industry or community relationships that lead to service-learning opportunities, student internships, community partnerships, or external grant proposals.
Ability to engage students in data storytelling through data visualization.
Willingness to cover data visualization theories and methods using both scientific and creative techniques.
Ability and desire to learn and teach multiple data visualization theories or methodologies across arts, humanities, social sciences, liberal arts, public health, and STEM.
Applicants with qualitative research methodology backgrounds may be considered if able to teach basic quantitative methods in addition to qualitative methods.

Responsibilities

Teach DATA 1010: Introduction to Data, covering fundamental data literacy, analysis, communication, and ethics.
Teach DATA 2030: Data Visualization, covering theories and techniques for understanding and developing visualizations such as graphs, charts, maps, and interactive tools.
Teach at least one multidisciplinary undergraduate course in the professor’s area of interest or expertise.
Develop or add courses to the existing curriculum, potentially in non-standard data types, survey and polling methods, biostatistics, bioinformatics, experimental data, or AI literacy.
Develop and facilitate CAIDS programs and services that increase faculty and student participation in data-related research.
Depending on expertise, develop and coordinate internship and research-assistant programs; facilitate collaborative grant applications; provide statistical, data, or applied-AI consulting; coordinate seminars, workshops, data-thons, and poster sessions; and support interdisciplinary research groups.
Work with Howard-Tilton Memorial Library to plan the Love Data Week Data Visualization and Physicalization competitions and exhibits.
Promote and coordinate student participation in Tulane poster sessions and develop and facilitate occasional data visualization workshops.
Contribute to CAIDS priorities and future direction and actively partner in managing institute programs and initiatives.
Optionally conduct data-related research or scholarship on data science teaching and learning as part of the workload.