Data Scientist (Data Science and Evaluation Theme) at African Population And Health Research Center (APHRC)

Qualifications, Skills, and Experience

• PhD in data science, applied mathematics, computational science and engineering, applied statistics or other related field. A master’s degree in any of the following mathematics, statistics or computer science.
• A minimum of seven years of professional experience in data analytics, computer science or statistics; with at least one-year’s postdoctoral experience.
• Programming skills. Knowledge of statistical programming languages like R, Python, and database query languages like SQL, Oracle, Hive, Pig is desirable. Familiarity with Scala, Java, or C++ is an added advantage.
• Statistics. Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for datadriven activities.
• Machine learning. Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests is essential
• Strong math skills (Multivariable Calculus and Linear Algebra) to support predictive performance or algorithm optimization techniques.
• Data wrangling. Proficiency in handling imperfections in data is a critical aspect of this role.
• Experience with data visualization tools like R shiny, matplotlib, ggplot, d3.js.ArcGIS, QGIS, PowerBi, Excel, 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, has attention to detail and a strong analytical mind.
• Demonstrates 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; have organizational and time management skills to manage and prioritize workload.
• Demonstrate an appreciation of technical and analytic challenges, and learning new approaches and topics.