Data Scientist at African Population And Health Research Center (APHRC)

Duties/Responsibilities

The Data Scientist will be responsible for using novel data science tools including machine learning (ML) and artificial intelligence (AI) to address APHRC’s research questions. They will be expected to expand the use of advanced analytics and data science in the DSE and APHRC.

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.
• 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.
• 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.