Data Science Education Postdoctoral Scholar

North Carolina State University

Postdoctoral Fellowship

The Data Science Academy (DSA) offers Postdoctoral Fellowship opportunities based on specific research projects, academy goals and grant funding.

Data Science Education Research on the ADAPT Course Model

With data science growing as a career path, professional skill and component of literacy, the NC State Data Science Academy (DSA) is creating a model for training postdoctoral fellows that centers diversity, equity and inclusion. This program will bring together mentors with varied backgrounds, professional expertise and research interests with postdoctoral fellows from groups that are underrepresented in STEM and education (e.g. women, BIPOC) and may have limited experience in data science or education research. The DSA aims to engage learners at any level and with varying career goals by offering data science courses, analytics consulting and research enablement. DSA courses are based on the All-campus Data science through Accessible Project-based Teaching and learning (ADAPT) model, which is designed to support all learners with low barriers to entry, multiple elements of choice and high probability of success. The DSA is expanding course offerings and its roster of instructors while launching an ADAPT model study.

The DSA will train a cohort of four postdoctoral fellows, connecting them with mentors who specialize in education research and can support them in developing their own research. Research and professional development activities will include: weekly mentor meetings, leadership development, teaching meetings, a data science education journal club, brown bag lunches, presentations, workshops and a data science education conference. Fellows may also access NC State professional development programs and funding for travel and professional memberships.

The postdoctoral cohort will have the opportunity to develop competencies in STEM research methods by building their own areas of focus in an ongoing research study to better understand the impact of the ADAPT course design model on student identity, affinity for data science and intent to pursue future courses, internships and data science-related jobs.


The DSA will create a model for training postdoctoral scholars in data science education research and successfully inducting them into the data science education and STEM-Ed research community. This model will be developed with a focus on diversity, equity and inclusion to support specifically the training and professional development of women and other groups who are currently underrepresented in STEM and STEM education. As the postdoctoral fellows enter the workforce, this project can be expected to inform the induction of data science education research, curriculum development and student experience. The ADAPT course model, which will be continuously improved by research, can be adopted and studied at other institutions and in other disciplines.

Application Process

Applicants for the DSA Postdoctoral Program will need to provide a long Curriculum Vitae, along with a single integrated teaching and research statement. In this statement, candidates should describe five projects (e.g. research, teaching, curriculum development, industry/government work experience or outreach) that demonstrate their readiness for postdoctoral work and how their research interests will align with DSA goals and objectives. This statement will also need to clearly demonstrate how their projects, methods and evaluation are informed by and support Accessibility, Diversity, Equity, and Inclusion. Applications will be evaluated on the following criteria, which will be shared with the applicants:

  1. Why is the applicant interested in a postdoc with the DSA?
  2. How do the five projects demonstrate the applicant’s skills as a communicator?
  3. Are the applicant’s projects and evaluation metrics informed by goals and practices of accessibility, diversity, equity, and inclusion?
  4. What is the impact of this work, and how is impact measured?
  5. How is the applicant’s work grounded in data science and/or data science education? If not, how did past projects prepare the applicant to engage with data science?
  6. How do the applicant’s projects connect to STEM-Ed and STEM-Ed research?
  7. Are the interests of the applicant in areas the DSA mentors are well-prepared to mentor?

Applications will be accepted via

Department Required Skills

Equivalent education/experience can substitute for all minimum qualifications except when there are legal requirements, such as a license/certification/registration.
  • Must be a U.S. citizen, national, or permanent resident when the application is submitted.
  • Must have earned a doctoral degree, or expect to have earned the doctoral degree in a field of Science, Technology, Engineering, or Mathematics (STEM), STEM Education, Education, or a related discipline prior to the start date of the position. The start date must be no later than two years after the conferral of the doctoral degree.
  • Must not hold a tenure-track position.
  • Experience with data science, data science education and/or data science education research.
  • At least 3 years of relevant teaching experience in high school, postsecondary level or as an instructional trainer.
  • Relevant research experience. We encourage applications from a broad range of disciplines, including statistics, mathematics, computer science, education, engineering, policy, ethics, communications, arts and humanities, physical and social sciences, or related fields.
  • Strong communication and writing skills.

Preferred Years Experience, Skills, Training, Education

Qualified applicants may not have all of the following:
  • Record of research or professional publication and presentation, commensurate with experience.
  • Prior work in research projects with human subjects.
  • Experience with accessibility practices in communication.
  • Experience with project-based teaching and learning.
  • Experience broadening participation in STEM education and STEM fields.
  • Experience with quantitative and/or qualitative data analysis and communication tools.
  • Demonstrated success working in collaborative teams.

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