Research Fellow (Health Data Analytics) at Aga Khan University (AKU)

Responsibilities

• Support the DMAC lead and Manager in the day to day administrative functions
• Provide technical contributions in a fast paced team environment to accelerate our efforts on building an analytics driven product pipeline
• Perform independent statistical analysis, computer programming, predictive modelling, and contribute to new studies
• Build cross-functional relationships to collaboratively partner with the other collaborators and effectively network within the Data Science community
• Use advanced mathematical models and machine learning to deliver insight, recommendations and solutions to different projects within UZIMA-DS project
• Contribute to the development of code, computational tools and databases for mining and visualizing large data sets resulting from maternal newborn and child health as well as mental health studies
• Develop sustainable, consumable, accurate and impactful reporting on model inputs and outputs
• Use the project data and resources to develop a PhD thesis and scientific papers leading to the award of the doctoral degree

Requirements

• Holder of Master’s Degree and currently pursuing a PhD in Computer Science, Statistics/Biostatistics, Mathematics or equivalent in a recognized university
• Proficiency using one or more programming or scripting languages such as Python, R, MATLAB, Perl, SAS or C#
• Skills in Artificial Intelligence/Machine Learning methods, data integration and mathematical modeling as well as proficiency in ETL tools
• Experience in data management in relational databases such as MySQL
• Experience in data analysis using STATA, R, or other statistical software
• Experience working with longitudinal studies, handling large data sets and cloud computing: GCP, MS Azure is desirable
• Experience in visualizing/presenting data for stakeholders and the use of relevant data visualization tools
• Experience with relevant technologies e.g. Tensor Flow, Apache Flume, machine learning libraries
• Relevant professional experience with applied data analytics and predictive modeling