Jacob Saikolo is an experienced data scientist and statistician working as a Data Manager at the Institute of Public Opinion and Research
(IPOR), bringing over six years of experience in research, electronic survey tools
programming, data management, facilitating fieldwork research trainings and
short courses, real-time data monitoring, data analytics and visualizations, big
data analytics, computer programming, technical field support, report writing,
results dissemination, and digital communications. He holds a Master of Science
in Data Science from Manipal University in India and a Bachelor of Science in
Statistics and Computer Science from the University of Malawi.
With a strong academic foundation and practical
experience, Jacob specializes in end-to-end data systems and methodologies with
a focus on data quality aspects. He is proficient in designing and implementing
CAPI, CAWI, and CATI data collection systems with hands-on experience with
top-notch tools in data management like R,
Python, Stata, SPSS, SAS, Excel, Power BI, SQL, Google Data Studio, QGIS, and multiple electronic
survey platforms, including SurveyCTO, SurveyToGo, ODK,
KoboToolbox, Survey Solutions, CSPro, DHIS2, and REDCap. He has technical skills in programming
languages including Python, Java, JavaScript, ReactJS, PHP, HTML, and CSS. He has experience working with big data tools
such as Apache Spark and Hadoop for distributed data processing and analysis,
and he has a strong foundation and experience in machine learning, deep
learning, computer vision, and natural language processing using frameworks
like TensorFlow, Keras, and Scikit-learn.
Jacob previously served as a part-time
lecturer in statistics and computer science at the University of Malawi for
four years, teaching courses like statistical hypothesis testing, linear regression, time series analysis, descriptive statistics, and
categorical data analysis. He has co-authored five publications on public
health data. He leverages data and computational methods to generate rigorous
evidence for research, policy, and decision-making.
I’m not only interested in Statistics and Data Science, but also its impact in society and how they can be used to help improve the conditions of humans, society, and beyond. My research areas are in the fields of big data, data wrangling, data quality, statistical methodology, and AI in health and governance.