PakistanFull TimeMid-levelPosted Today
Research Specialist (Data Scientist), Paediatrics & Child Health - MC - (260002Y2) Introduction The Outreach and Research Programme of the Department of Paediatrics and Child Health has ten community-based field sites across Pakistan (including four in Karachi) and focuses on research for betterment of women and child health. The Department has a Nutritional Research Laboratory as well as an Infectious Disease Research Laboratory that cater to the needs of its research projects. It has well-acclaimed work to its credit both at the national and international level. 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. Responsibilities You will be required to support the planning, implementation, coordination, and evaluation of AI and health research projects by coordinating research activities, conducting data analyses, ensuring data quality, and contributing to scientific outputs in collaboration with multidisciplinary teams. Specific responsibilities include:
- coordinate day-to-day implementation of AI and health research projects.
- track project timelines, milestones, risks, and deliverables.
- coordinate meetings with investigators, collaborators, and implementation partners and maintain project documentation and records.
- assist in data cleaning, validation, and management of clinical and research datasets.
- perform statistical analyses using R, Python, STATA, or equivalent analytical software.
- prepare datasets for AI and machine learning applications while ensuring data quality, completeness, and integrity.
- support the development, testing, and evaluation of AI and machine learning models under the supervision of senior team members.
- assist in data annotation, preprocessing, model validation, and evaluation of AI tools in clinical and community settings.
- participate in literature reviews and evidence synthesis related to AI and digital health.
- liaise with clinicians, researchers, data scientists, and implementation partners.
- coordinate workshops, stakeholder meetings, training sessions, field visits, and implementation activities as required.
- stay updated with advances in AI, digital health, epidemiology, and implementation research and contribute to continuous improvement of research processes.
- a Master's degree in Data Science, Health Data Science, Computer Science, or a related field.
- at least 2 years of experience in health research, data analysis, implementation research, digital health projects, or related areas.
- experience working with clinical or public health datasets; experience in AI, machine learning, or health informatics will be an advantage.
- proficiency in cleaning, validating, and managing clinical and research datasets.
- proficiency in statistical analysis using R, Python, STATA, or similar analytical software.
- knowledge of data preparation techniques for machine learning and AI applications.
- understanding of data quality assurance, data governance, and research data management principles.
- ability to develop dashboards, analytical reports, and data visualisations.
- basic knowledge of AI and machine learning concepts, including model development, validation, and evaluation.
- familiarity with data annotation, preprocessing, and management of clinical datasets.
- knowledge of AI tool evaluation in clinical and community health settings.
- strong project coordination and organisational skills, with the ability to manage multiple activities and timelines.
- excellent interpersonal, written, and verbal communication skills, with the ability to work effectively in multidisciplinary teams.
commitment to research ethics, confidentiality, and Good Clinical Practice (GCP).