Curriculum Vitae

Author

Moses Mburu

Published

April 9, 2024

Moses Mburu

Location: Kilifi, Kenya

LinkedIn: https://www.linkedin.com/in/mmburu/

Blog: https://m-mburu.github.io/

Github: https://github.com/m-mburu

Professional Summary

A seasoned Data Analyst with extensive experience in data management, statistical analysis, and machine learning. Having worked across healthcare, financial research, and agricultural sectors, I possess a robust understanding of various analytical methodologies and tools, including R, Python, and SQL. My career is marked by a keen focus on leveraging data to address complex issues, complemented by a continuous pursuit of emerging technologies to enhance efficiency in data-driven decision-making.

Employment History

Data Scientist, The Foundation for Innovative New Diagnostics (June 2022 – Present)

  • Managing and ensuring the integrity of biobank data through the development of reproducible data management processes and R scripts.
  • Developing and deploying interactive Shiny and Power BI dashboards for internal and external stakeholders, integrated within the company’s SharePoint wiki page.
  • Leading the coordination with external partners and consultants to enhance data management and biobank activities, ensuring project objectives are met timely and effectively.
  • Collaborating with the Data Science team to author R packages and enhance data science operations through strategic use of GitHub for code management.
  • Contributing to scientific publications and reports, assisting with data analysis and presentation for internal and external audiences.

Data Manager, KEMRI WELLCOME TRUST (May 2019 – May 2022)

  • Carry out data review, validation including discrepancy checking, and cleaning.
  • Manage data entry staff (permanent or casual) in collaboration with Administration and PIs and monitor performance.
  • Design database for capturing and storing data.
  • Prepare performance indicator reports on data status as study project progress. Co-ordinate data for all study sites projects.
  • Conduct preliminary analysis and generate study progress reports.
  • Participate in the development, review and translation of research tools; Participate in the pre-testing of data collection platforms;
  • Data cleaning including doing all required consistency checks for project data;
  • Document project data sets according to the Centre’s guidelines on data documentation, archiving and sharing;
  • Extract data and prepare analytical files;
  • Develop template syntax files for research staff to use in creating various data sets;
  • Perform basic and advanced statistical analysis of data using tools such as STATA and R.

Data Analyst/Statistician, Low Income Financial Transformation (June 2016 – May 2019)

  • Preparing all ‘R’ scripts in a way that they are usable by others (documenting and standardizing code)
  • Supporting publications such as blogs, Research Pearls and larger projects with data analysis
  • Identifying interesting patterns in the data and making suggestions for publications, e.g. differences between diaries-and-non-diaries respondents, differences between baseline and endline
  • Voicing any data discrepancies and suggest solutions to any problems these might cause
  • Suggesting improvements on data processing or analysis techniques
  • Supporting others on methodological issues such as sample design, sample selection
  • Working together with Maria Helmrich on working out data tasks, e.g. correlations analyses
  • Supporting any project with sampling and other statistical expertise

Qualifications

Education

  • Bachelor of Science in Statistics, University of Nairobi, 2012 – 2016
    • Specialized in Statistical Modelling, Data Analysis, and Time Series Analysis.

Certifications

  • Spatial Data, Data Camp: Focused on visualizing and analyzing spatial data using modern tools and techniques.
  • Machine Learning, Coursera: Mastered both supervised and unsupervised learning methods, encompassing various regression, classification, and clustering techniques.
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Publications

  • Gachoki, P., Mburu, M., & Muraya, M. (2019). “Predictive Modelling of Benign and Malignant Tumors Using Binary Logistic, Support Vector Machine and Extreme Gradient Boosting Models.” American Journal of Applied Mathematics and Statistics, 7(6), 196-204. DOI: 10.12691/ajams-7-6-2

  • Maleche-Obimbo, E., et al. (No date). “Magnitude and factors associated with post-tuberculosis lung disease in low- and middle-income countries: A systematic review and meta-analysis.” PLOS Global Public Health. Available at: https://journals.plos.org/globalpublichealth/article?id=10.1371%2Fjournal.pgph.0000805 (Accessed: 09 October 2023).