Assistant Professor - Data Science

The

B.Sc. Data Science programme at Symbiosis University of Applied Sciences is an industry-oriented, multidisciplinary undergraduate programme that prepares students from diverse academic backgrounds - mathematics, commerce, humanities, economics, and the biological sciences - for careers in data science, artificial intelligence, analytics, and emerging technologies. The curriculum builds strong foundations in mathematics, statistics, and programming, and progresses through applied machine learning, deep learning, natural language processing, generative and agentic AI, data engineering, cloud computing and supported throughout by industry projects, internships, and experiential learning.
Purpose of the Role
The Assistant Professor will play a central role in delivering a future-ready, industry-aligned

B.Sc. Data Science programme. The role combines high-quality teaching, applied research, student mentoring, industry engagement, and active contribution to programme design and institutional development. The successful candidate will help produce graduates who are technically rigorous, ethically grounded in their use of AI, and genuinely ready to contribute to the workforce from the day they graduate.
2.Teaching Responsibilities
The faculty member will teach a combination of subjects drawn from:
Python Programming, Data Analytics, Mathematics for Data Science, Statistics and Probability, Machine Learning, Deep Learning, Artificial Intelligence, Generative AI and Agentic AI, Cloud Computing, Computer Vision, Data Engineering, Database Management Systems, and Natural Language Processing, depending on programme need and individual expertise.
Deliver lectures, tutorials, laboratory sessions, and skill-based practical training across assigned courses
Design and deliver courses within the Outcome-Based Education (OBE) framework, including Course Outcomes (COs), CO-PO mapping.
Prepare and continuously update course material, lab manuals, case studies, and assignments that reflect current industry practice and tooling
Evaluate assignments, laboratory work, projects, and examinations fairly, consistently, and within institutional timelines
Adopt active learning, project-based learning, and AI-assisted pedagogy where appropriate to deepen student understanding
Contribute to periodic curriculum design and revision in consultation with the Academic council.
Ensure assessment integrity and timely processing of grades and results
3.Research and Scholarly Expectations
Publish in peer-reviewed journals and conferences (Scopus, SCI, or UGC-CARE listed) on a sustained basis
Pursue funded or sponsored research, consultancy, and collaborative projects with industry or government bodies
Maintain an active researcher profile (Google Scholar, ORCID, Scopus ) and work toward sustained citation growth
Contribute to patents, technical case studies, or open-source work where relevant to the discipline
Participate in and help organise Faculty Development Programmes (FDPs), workshops, and seminars that build scholarly capability across the department
4.Student Mentoring and Project Supervision
Serve as a faculty mentor and academic advisor for an assigned cohort of students, tracking academic progress and wellbeing
Supervise semester-wise mini-projects, capstone projects, dissertations, and applied research projects
Guide student teams participating in hackathons, datathons, case competitions, and innovation challenges
Support student participation in conferences, technical competitions, and industry certifications
5.Industry Collaboration and Placement Support
Build and maintain relationships with companies for internships, live industry projects, and full-time placements
Coordinate internship placements and evaluate outcomes in collaboration with industry mentors and supervisors
Organise guest lectures, industry visits, and expert sessions that keep curriculum and students connected to current practice
6.Administrative and Institutional Responsibilities
Contribute to NAAC/NBA accreditation documentation, self-study reports, and ongoing quality assurance processes
Support laboratory setup, upgrades, and ongoing management, including software and tool licensing
Assist with admissions outreach - school visits, webinars, open days, and content for brochures and the website
Collaborate across departments on interdisciplinary courses, electives, and joint academic initiatives
Maintain accurate academic records, including attendance, evaluation, course files, and OBE documentation
7.

Preferred Qualifications and Industry Experience
Two to six years of combined experience in teaching, applied research, or industry roles in data science or AI; candidates with strong industry project experience are encouraged to apply even without prior teaching experience
Demonstrated experience designing or delivering project-based, OBE-aligned, or industry-linked curricula

8.Required Technical Competencies
Competency Area
Representative Tools and Concepts

Mathematics & Statistics
Linear algebra,…