Assistant Professor Data Science / Statistics, University of Bristol, Mumbai

Role Overview

Leadership, Culture, and Engagement

• Participate in faculty committees, academic events, student recruitment, and quality assurance processes.

• Contribute to institutional development and participating in training and capacity-building initiatives.

• Build industry and/or academic partnerships that enhance the University’s visibility and engagement with external stakeholders.

• Work collaboratively with Professional Services partners to deliver delegated administrative and governance duties.

Teaching

• Design and deliver undergraduate and postgraduate courses ranging from mathematical statistics and machine learning to coding and algorithmics.

• Contribute to curriculum development initiatives and contribute to academic programme design in collaboration with senior colleagues and faculty teams.

• Contribute and promote innovative teaching strategies that improve student experience, transferable skills creation and employability prospects.

• Play a role in curriculum development and contextualisation, programme accreditation, programme enhancement and the maintenance of quality assurance standards within the university.

Research

• Supervise student research projects, theses, and internships, providing academic guidance and mentoring.

• Build an excellent record of high-quality publications in reputable, peer-reviewed academic journals, or recognised discipline-specific outputs, actively contributing to the international discourse inData Science / Statistics.

• Pursue research funding and build interdisciplinary collaborations across the university network.

Essential Criteria

• A completed PhD inmathematical statistics and data science.

• Evidence of ability to teach to a very high standard at undergraduate and postgraduate levels

• An emerging track record of high-quality scholarly research, with potential to publish in highly ranked journals.

• Evidence of excellent organisational and administrative skills.

• Excellent communication, leadership, and collaborative skills.

• Demonstrates a proactive approach to professional development.

• Strong programming expertise.

• Proven experience in machine learning and data science.

• 
  Between 2 and 10 years of academic experience post-PhD

• Post-PhD experience in teaching, research, or a combination of both in a higher education setting

• Strong mentoring, academic advising, and supervisory skills.