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.

1. 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 ID) 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, probability theory, statistical inference, hypothesis testing, regression analysis

Programming

Python (required); R and SQL (desirable); software engineering fundamentals

Machine Learning & Deep Learning

scikit-learn, TensorFlow, PyTorch; CNNs, RNNs, Transformer architectures

Artificial Intelligence

Search and optimisation, knowledge representation, classical AI techniques

Generative AI & Agentic AI

Large language models, prompt engineering, retrieval-augmented generation, multi-agent frameworks (LangChain, AutoGen, CrewAI)

9.Preferred Certifications

• AWS Certified Machine Learning – Specialty, or AWS Certified Solutions Architect
• Microsoft Certified: Azure AI Engineer Associate, or Azure Data Scientist Associate
• Google Professional Data Engineer, or Google Professional Machine Learning Engineer
• Deep Learning Specialisation (DeepLearning.AI) or an equivalent recognised credential
• NPTEL or Coursera certifications in AI, ML, or Data Science from IITs or leading global universities
• TensorFlow Developer Certificate
• Certified Analytics Professional (CAP) or an equivalent certified data science credential - desirable but not mandatory