Assistant/Associate Professor in Statistical Data Science

Assistant/Associate Professor in Statistical Data Science

Heriot-Watt University - School of Mathematical and Computer Sciences

Edinburgh, United Kingdom

Detailed Description

Heriot-Watt University invites applications for an Assistant or Associate Professor in Statistical Data Science, with a specialisation in health and medical applications. This role is pivotal in advancing the University’s research and teaching capabilities in statistical data science, with a particular focus on applied statistics and statistical machine learning within healthcare.

The successful candidate will have the opportunity to shape the curriculum, drive impactful research, and contribute to public health policy and outcomes through innovative data science applications.

Located in the School of Mathematical and Computer Sciences, Department of Actuarial Mathematics and Statistics, at Heriot-Watt University and working closely with the University’s Global Research Institute in Health and Care Technologies, this position will provide access to interdisciplinary resources and collaboration opportunities with leading researchers and industry partners.

The anticipated start date is September 2026.

Key Duties and Responsibilities

Research Leadership

• Lead and contribute to large-scale research projects focused on applying statistical data science to health and medical data, addressing critical challenges in healthcare.
• Secure research funding from research councils, industry partners, and health-related organisations, building an independent funding portfolio.
• Build and sustain collaborations with interdisciplinary teams across departments such as Biomedical Engineering, to drive impactful research that influences healthcare policies and public health outcomes.
• Disseminate research findings through high-impact journal publications, conferences, and public engagement activities.

Teaching and Curriculum Development

• Develop and deliver new postgraduate taught programmes related to statistical data science, targeting numerate students from diverse fields, such as the health sciences, biology, psychology, and urban planning.
• Innovate curriculum content in statistical data science, focusing on health data applications, machine learning, epidemiology, and geospatial data science.
• Supervise graduate and postgraduate students, supporting their research projects and career development, with a specific focus on health-related data science.
• Ensure that teaching methodologies incorporate real-world health data, enhancing students' practical skills in applying statistical methods in healthcare contexts.

Interdisciplinary and Industry Collaboration

• Establish and maintain partnerships with healthcare organisations and industry stakeholders, advancing the University’s contributions to healthcare innovation and public health improvements.
• Engage in consultancy and collaborative projects with public health bodies, such as NHS and Public Health Scotland, providing research evidence that informs and shapes public health policies.
• Position the University as a leader in statistical data science in healthcare, enhancing its research impact, innovation, and influence in public health.

Public Engagement and Community Impact

• Participate in public outreach initiatives, sharing research insights that contribute to the societal understanding of health data science.
• Actively engage in professional organisations and community health projects, fostering public engagement and enhancing the University’s visibility in health and care innovation.
• Influence public health policies by providing expert consultation and evidence-based insights from research.

The successful candidate will be based at our Edinburgh campus in the UK. We encourage applications from under-represented groups. We welcome requests for flexible working arrangements and normally accommodate them.

Education, Qualifications and Experience

As the successful candidate, you will lead, carry out and publish internationally excellent research and teaching in your field. You will have a strong track record of research in statistical data science - which may also include Bayesian statistics, machine learning, applied statistics and epidemiology - as demonstrated through publications, citations, external invitations and research funding. You will be established as an international research leader, with the ambition to build a world-class academic group and have the experience or potential to supervise PhD students and post-doctoral researchers.

Essential

• PhD in statistical data science, applied statistics, epidemiology, or a closely related field.
• Strong research background in statistical data science with applications to health or medical data, evidenced by a track record of high-impact publications.
• Experience in securing research funding, ideally with a focus on health-related data science.
• Demonstrable teaching experience, with a commitment to developing and delivering data science programmes tailored to interdisciplinary and healthcare applications.
• Proven ability to collaborate effectively with diverse stakeholders, including academic colleagues, industry partners, and public health organisations.
• Excellent communication skills, with the ability to engage students, colleagues, and the wider community.

Desirable

• Established network within healthcare or health-related research communities.
• Familiarity with interdisciplinary approaches and translational research in health data science.
• Experience in mentoring and supervising postgraduate research students.

Key Performance Indicators

• Research Output: Annual publications in high-impact journals, research grants secured, and impactful interdisciplinary research projects developed.
• Teaching Excellence: Positive student evaluations, number and quality of new courses developed, and successful launch and growth of new MSc programmes.
• Industry Engagement: Sustained partnerships with healthcare and industry stakeholders, evidenced by collaborative projects and consultancy engagements.
• Public Health Impact: Contributions to improved healthcare outcomes and policies through applied research and public outreach.
• Professional Development: Active participation in relevant workshops, certifications, and continuous education in data science and health informatics.