Assistant Professor in Data Analytics

Roles and responsibilities

• A Ph.D. in Data Science/ Analytics or related fields from an internationally reputable university
• Outstanding research record in the field of data science, which is demonstrated by publishing in top international journals and conferences (Scopus and SCIE)
• Strong assurance of distinction in teaching, curriculum development, and learning outcome assessment
• Experience in teaching data science and computing courses at the undergraduate level
• Demonstrated ability to collaborate effectively with colleagues in performing professional service.
• Ability to communicate effectively in English
• Excellent interpersonal skills.
• Industrial Experience in providing statistical consultancy to researchers and the public
• Open to cross-disciplinary cooperation in research, teaching
  

Essential Duties And Responsibilities

The selected applicant will be expected to:

• Develop and teach undergraduate in Data Science/ Analytics and Computer Science.
• Engage in innovative and technology-based approaches to learning and teaching.
• Publish high-quality research in leading internationally recognized journals in his/ her own field.
• Participate in committees at the departmental, college, and/or university levels as assigned.
• Actively engage in promoting the growth of the Ajman University.
• Ability to provide consultancy services to researchers and the business community.
• Actively seek out methods, procedures, and resources to best achieve course and lesson objectives.
• Perform miscellaneous job-related duties as assigned
  

Application Process

A completed application will include:

• A letter of interest addressing qualifications for the position.
• A current curriculum vitae.
• A statement of teaching philosophy and research.
• Copies of transcripts of all graduate coursework.

Desired candidate profile

1. Teaching and Instruction

• Course Development and Teaching: Designing and teaching undergraduate and graduate-level courses related to data analytics, such as statistical analysis, data mining, machine learning, data visualization, big data technologies, and predictive analytics. The courses may also cover specific tools such as Python, R, SQL, Hadoop, and Tableau.
• Lectures and Classroom Management: Delivering lectures, seminars, and workshops, making complex data analytics topics accessible and engaging. Encouraging active student participation and fostering a learning environment that promotes critical thinking and hands-on practice with data analytics tools.
• Lab Supervision and Projects: Overseeing lab sessions where students apply analytical techniques to real-world datasets. Supervising student projects that involve gathering, cleaning, analyzing, and interpreting data to solve business or scientific problems.
• Mentoring Graduate Students: Supervising graduate students working on theses, dissertations, or research projects. Guiding students through the process of data collection, analysis, interpretation, and presentation of results.

2. Research and Scholarly Activity

• Conducting Research: Engaging in original research in the field of data analytics. This could involve the development of new algorithms, tools, or methodologies for analyzing data, as well as exploring new applications for data analytics in fields such as healthcare, finance, business, or social sciences.
• Research Collaboration: Collaborating with faculty members, industry partners, and other researchers to conduct interdisciplinary research that uses data analytics to address complex problems. Building partnerships with companies or research institutions for data-driven projects.
• Publishing Research: Writing and publishing research findings in reputable, peer-reviewed journals, conferences, or books. Establishing a strong research profile that contributes to the advancement of the data analytics field.
• Grant Writing: Applying for research funding from government agencies, industry partners, and private foundations to support ongoing research initiatives in data analytics.
• Data-Driven Solutions: Exploring real-world applications of data analytics, including developing models that predict trends, optimize processes, or improve decision-making in different industries (e.g., healthcare analytics, financial forecasting, or supply chain optimization).

3. Professional Development and Industry Engagement

• Staying Updated with Industry Trends: Keeping up with the latest trends, tools, and technologies in data analytics, such as emerging machine learning algorithms, deep learning, and advancements in big data analytics and artificial intelligence (AI).
• Industry Collaboration: Partnering with companies, non-profits, or government agencies to apply data analytics to real-world problems. Offering consulting services or working on industry-sponsored research projects.
• Professional Networking: Attending and presenting at academic and industry conferences, contributing to workshops, and networking with peers in the data science and analytics communities.
• Continuing Education: Participating in professional development activities, such as attending workshops, webinars, or certification programs, to keep skills current and contribute to thought leadership in the field.

4. Service to the Institution and Academic Community

• Committee Work: Serving on academic committees, such as curriculum development committees, research committees, and student evaluation committees. Contributing to the overall governance and strategic direction of the department or college.
• Peer Review: Participating in peer review for academic journals and conferences, reviewing research papers and proposals in the field of data analytics.
• Advising Students: Advising students on academic and career matters, guiding them in choosing research topics, internships, and career paths in data science and analytics.

5. Leadership and Administrative Roles

• Curriculum Development: Helping develop and review the data analytics curriculum to ensure that it reflects the latest trends in the field and meets the needs of both students and industry.
• Research Leadership: Taking a leadership role in organizing research projects or collaborating with other departments or research centers within the institution. Mentoring junior faculty or new researchers in the field.
• Student Recruitment: Participating in recruitment efforts by promoting the data analytics program to prospective students. Assisting in the selection of graduate students for admission into the program.