Faculty - Department of Community Health Sciences at Aga Khan University Hospital

Faculty - Department of Community Health Sciences

Entity: Medical College

Location: Karachi, Pakistan

Introduction:

Aga Khan University's (AKU) Department of Community Health Sciences (CHS) is a preeminent public health institution in the country. The vision of the Chancellor set the direction for the University to take a lead in community development and the creation of an innovative curriculum that emphasizes a balance between patient care and population health. Over the past several decades, CHS has played a major role in leading the development of national level primary health care, public health practice, health systems through evidence-based approach. CHS has established successful models in urban and rural settings in Pakistan emphasizing empowerment - building on the strengths of communities. As part of an international university, CHS collaborates in a global network of outstanding research and teaching institutions including AKU affiliates in East Africa and Central Asia.

CHS has five major sections – Epidemiology & Biostatistics, Health Policy & Management, Population & Reproductive Health, Environmental-Occupational and Climate Change (EOHCC), and Non-Communicable Diseases & Mental Health.

As an equal opportunity employer, AKU believes in promoting a diverse and inclusive culture and is committed to adopt appropriate standards for safeguarding and promoting a respectful relationship with and between diverse workforce of its faculty, staff, trainees, volunteers, beneficiaries, wider communities, and other stakeholders with whom it works, including children and vulnerable adults and expects all employees/trainees and partners to share this commitment.

Aga Khan University invites applications for Faculty in the Department of Community Health Sciences.

Responsibilities:

• Design and deliver courses in health data science, machine learning, AI applications in health, statistical programming, and data visualization.
• Develop and update specialized training modules in big data analytics, predictive modeling, and computational methods.
• Supervise and mentor graduate students, ensuring high-quality applied research, publications, and innovative use of real-world health datasets.
• Lead or contribute to research programs in advanced analytics, AI, digital health, and computational modeling.
• Support grant writing and contribute to securing research funding in health data science and analytics.
• Build collaborations with national and international partners across academia, health systems, and industry to strengthen applied health data science capacity.
• Translate complex data into evidence-based insights for health policy, systems strengthening, and population health improvement.

Requirements:

Applicants should demonstrate strong expertise in health data science, advanced analytics, AI and machine learning, with a proven track record of applying these methods in health research.

• Strong record of research and publications in data science, machine learning, AI, or biostatistics applied to health.
• Proficiency in statistical and computational tools (e.g., R, Python, SQL, TensorFlow, PyTorch, Spark, cloud-based platforms).
• Demonstrated ability to teach using innovative, technology-enabled, and data-driven methods (experience in hybrid/online teaching is preferred).
• Experience in curriculum design, student mentorship, and supervision of applied research projects.
• Excellent communication skills and ability to work in interdisciplinary and collaborative environments.

Position-specific requirements:

Assistant Professor

• PhD or equivalent with at least two years of postdoctoral or research experience.
• Evidence of independent research and publications in health data science, biostatistics, or applied machine learning.

Senior Instructor

• MSc/MPhil/PhD in Data Science, Biostatistics, or a related field (MBBS with MSc/MPhil is also acceptable), with at least two years of relevant experience.
• Demonstrated interest in teaching and applied research in data science and analytics.

Comprehensive employment checks will be conducted.

*Only shortlisted candidates will be contacted.