Job Description
The African Population and Health Research Center (APHRC) is a leading Africa-based, African-led, international research institution headquartered in Nairobi, Kenya. APHRC conducts policy-relevant research on population, health, education, urbanization and related development issues in sub-Saharan Africa.
APHRC seeks to recruit an AI Software Engineer to work in the Data Science and Evaluation (DSE) Theme within the Research Division.
Duties/Responsibilities
The AI Software Engineer will be responsible for creating deployable versions of all Machine Learning models and integration of these into products for improving health and well-being. They will join APHRC’s multidisciplinary team to help in shaping new strategy and showcasing the potential for AI through early-stage solutions.
The AI Software Engineer will:
• Develop ML models alongside DSE’s data engineers, data analysts, data scientists and provide provide end to end AI solutions;
• Build Code Infrastructure from ML models developed by data scientists in DSE using advanced technologies;
• Package ML models into usable products by researches and policy makers;
• Create End Point APIs from the ML algorithms. These will include products such mobile applications, web services, chatbots, CDSS etc.;
• Develop the development of platform for data sharing;
• Provide updates on breakthrough artificial intelligence technologies with the potential to transform the Center’s research environment, the research staff or policy makers’ experience and influence policy development and decision making;
• Work closely with DSE data engineers, program and data managers to produce data into usable formats for analysis;
• Prepare and support monthly reports and scientific publications;
• Support data managers in the use of the metadata software programs;
• Develop training curricula and training materials;
• Attend DSE technical and progress meetings; and
• Contribute to report and manuscript writing, knowledge translation products, grants, and ethics review board applications.
Qualifications, Skills, and Experience
• PhD in Data science, Applied Mathematics, Computational Science and Engineering, Applied Statistics. Master’s degree in Computer Science, Data Science, Software Development, or other related field.
• At least three years of experience formulating and strategizing AI solutions; with at least one-year’s postdoctoral experience.
• Solid understanding of common programming languages used in AI, such as Python, Java, C++, and R.
• Advanced knowledge of statistical and algorithmic models as well as of fundamental mathematical concepts, such as linear algebra and probability.
• Experience working with large data sets and writing efficient code capable of processing large data streams at speed.
• Proven experience in applying AI to practical and all-inclusive technology solutions.
• Hands-on knowledge in machine learning, deep learning, Tensorflow, Python, NLP.
• Understanding of functional design principles, object-oriented programming principles, basic algorithms.
• Expertise in REST API development, NoSQL design, RDBMS design.
• Proven expertise in using deep learning, neuro-linguistic programming (NLP), computer vision, chatbots, and robotics to help the internal teams promote diverse research outcomes and drive innovation is a must have.
• Understanding of website scripts such as XML, Javascript, JSON.
• Understanding of ETL framework and ETL tools including Alteryx and Microsoft SSIS.
• Digital marketing analytics tools including Google 360, Google Analytics, Google Tag Manager and Adobe Marketing Suite.
• Experience with data visualization tools like R shiny, matplotlib, ggplot, d3.js.ArcGIS, QGIS, Tableau to visually encode data and generation of dashboards for interpretation.
• Good communication skills to describe findings to both technical and non-technical audiences.
• Excellent problem-solving skills, self-driven, has attention to detail with a strong analytical mind.
• Demonstrate ability to work both independently and to work collaboratively with internal and external team members, and stakeholders.
• Ability to multi-task, work accurately and effectively to deadlines; has good self-assessment of timing of tasks and ability to set deadlines. Organizational and time management skills to manage and prioritize workload.
• Demonstrate an appreciation of technical and analytic challenges, and learning new approaches and topics.