Data Science Faculty

Role & responsibilities

Key Responsibilities:

1. Teaching and Instruction:

• Develop and deliver lectures, seminars, and tutorials in their area of expertise.
• Prepare course materials (syllabi, assignments, exams) and manage grading.
• Mentor and guide undergraduate and graduate students, providing academic advice and support.
• Incorporate current research and real-world applications into the curriculum.
• Use a variety of teaching methods, including lectures, discussions, group work, and online learning platforms.
• Service and Administrative Duties:
• Participate in departmental, faculty, and institutional committees, contributing to academic governance.
• Assist with departmental administration, such as curriculum development, accreditation processes, and assessment of student performance.
• Serve as an advisor for student organizations or academic programs.
• Contribute to the universitys mission and strategic goals through service on committees or in special projects.

1. Professional Development:

• Engage in ongoing professional development, including attending conferences, workshops, and training.
• Stay current with developments in their field and incorporate new knowledge into teaching and research.
• Pursue opportunities for interdisciplinary collaboration and expand expertise in related fields.
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  Preferred candidate profile

Qualifications

Educational Requirements: M tech (Computer/Data Science) or equivalent terminal degree in the relevant academic discipline. Some institutions may accept candidates with a strong track record in research and professional experience, particularly in applied fields.

Experience:

• Previous teaching experience or freshers can also apply.

Skills:

• Strong communication and interpersonal skills for effective teaching and collaboration.
• Ability to manage multiple responsibilities and work independently.
• Knowledge of current trends and developments in the field.
• Familiarity with modern teaching technologies .
• Grant-writing and research funding experience may be required, especially for more research-intensive positions.