Terms of employment: Contract
Project duration: Four years
Duration of contract: one year with possible extension
Place: Ethiopian Public Health Institute, Addis Ababa
Reporting to: National Data Management Center for health (NDMC)
Number needed: Four
Background
The National Data Management Center for health (NDMC) at the Ethiopian Public Health Institute (EPHI) is a responsible center to centrally archive health and health related data, process and manage health research, apply robust data analytic technics, synthesis evidence and to ensure evidence utilization for decision making by the Federal Ministry of Health (FMoH) and other relevant stakeholders at local, sub-national and national and international levels. NDMC has collaborative partnership with Institute for Health Metrics and Evaluation (IHME), University of Washington and has established a Burden of Disease (BoD) Unit. The BoD Unit is responsible for data mapping, collecting, reviewing and archiving available health and health related data in the country and for producing national and subnational burden of disease estimates collaboratively with Global Burden of Disease (GBD) Study centered at IHME for population and demography, mortality and risk factors for a range of communicable diseases, non-communicable diseases, maternal newborn and child health, nutrition and for injuries. The unit creates platforms for translating BoD evidence for decision and policy at national and subnational levels. The NDMC is looking for high caliber staff for this collaborative project.
Roles and responsibilities
Apply different mathematical modeling techniques on data’s archived at EPHI.
Solve computational and analytic challenges by investigating the data, understanding the root questions, and coming up with alternative measurement strategies.
Implement code solutions in order to answer analytic questions, perform diagnostics on results, and test and assess methods.
Work under the senior data analyst and senior biostatistician to create, maintain, update databases containing health data from multiple sources such as surveys, vital registration systems, administrative records, and published studies relevant to NDMC research priorities
Carry out routine and complex computational processes and statistical modeling that are central to generating estimates of key indicators as guided by NDMC senior data specialist/biostatistician/health economist/public health experts.
Execute queries on databases and resolve intricate questions in order to respond to the needs of senior researchers and other stakeholders.
Bring together data, analytic engines, and data visualizations in one seamless com
Use protocols to identify problems with datasets and routine computational processes, rectify issues, and systematize data for analyses
Transform and format datasets for ongoing analyses.
Catalogue and incorporate datasets into databases.
Develop and implement algorithms to assess data quality.
Coding and re-coding data contained within various databases to identify patterns by compiling Excel spreadsheets and using Visual Basic for Applications
Analyze data accurately and presenting results in a clear manner
Data mining using state-of-the-art methods
Processing, cleansing, and verifying the integrity of data used for analysis
Developing and maintaining databases, reports, and maps
Organizing, manipulating and retrieving archived data for reporting, analysis, and presentation purposes
Extract data for analysis using standard NDMC protocols concepts, practices and procedures putational process.
BSc. Degree in Mathematics (Applied), Engineering, physics, Statistics, computer engineering and computation and related field of study with 0-year experience
Skills in computer programming and familiar with SQL, mySQL, Oracle and in developing code in R, Python, SQL, or other coding language.
Interest in health data analytics and data science
Demonstrated self-motivation, ability to absorb detailed information, flexibility, and ability to thrive in a fast-paced, energetic, highly creative and entrepreneurial environment.
Ability to learn new information quickly and to apply analytic skills to better understand complex information in a systematic way.
Strong quantitative and computational aptitude.
Robust problem-solving skills, along with a strong familiarity with data warehousing, data mining and data mapping
Capable of presenting and interpreting results
Data-oriented personality