Data Scientist Job (Data Science and Evaluation Theme) at African Population And Health Research Center (APHRC)… See details on how to apply for the opportunities available at African Population And Health Research Center.
Descriptions;
The African Population and Health Research Center (APHRC) is leading Africa-based, African-led, international research institution headquartered in Nairobi, Kenya, and conducting policy-relevant research on population, health, education, urbanization and related development issues on the continent.
RESPONSIBILITIES:
The Data Scientist will:
- Identify data sources for research needs, compile, collect, very relevant structured and unstructured data for analysis;
- Building Machine Learning and predictive models, ML algorithms to address data-driven research questions;
- Apply pre-processing steps including feature engineering, model selection, training and tuning for effective results;
- Work closely with data engineers, managers to produce data in to usable formats;
- Analyze data for trends and patterns, and find answers to specific questions;
- Set up data infrastructure, develop, implement and maintain databases;
- Generate information and insights from data sets, and identify trends and patterns;
- Prepare and support monthly reports and scientific publications;
- Create visualizations of data from the Center e.g. research generated data on micro portal;
- Train DSE members in robust data science techniques; 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 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 data- driven activities.
- Machine learning. Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests is essential.
