Onesmus Wachira Kabui

Data Analyst | [email protected] | +254 714 049 485 | onesmus.com | linkedin.com/in/onesmuskabui

About me

Data Analyst with nearly 18+ months experience in data analysis, data visualization, research design, and project management. Experienced in collecting and working with quantitative and qualitative data, transforming raw data into clear, actionable insights to inform decision-making. Proficient in Python, R, Excel, and SQL with a detail-oriented approach to analysis, insight extraction and communication. On my free time, I enjoy learning foreign languages.

Work Experience

Data Analyst, Kasi Insight

*Oct 2025 - Present

  • Data analysis and data visualization to unlock insights
  • Transforming data into actionable insights that inform decision-making
  • Project planning including research design for quantitative and qualitative data collection methods
  • Development of quantitative survey tools

Customer Service Data Analyst, Stan Interior Designers

September 2024 - September 2025

  • Apply data-driven insights to optimize customer service strategies and boost sales performance
  • Analyze customer interaction data to identify trends and implement improvements
  • Collaborate with sales teams to enhance customer satisfaction and revenue

Assistant Analyst, Ndima Tea Factory

Multiple internships: Oct 2020 - Dec 2020, Oct 2022 - Dec 2022, Mar 2023 - May 2023

  • Managed data entry for factory operations using ChaiPro (SQL-based)
  • Supported a critical transition to SAP ERP, ensuring 100% data integrity during the migration of production records
  • Analyzed production efficiency and quality trends, presenting insights to supervisors to inform decisions

Skills

  • Research design for quantitative and qualitative surveys; development and scripting of quantitative survey tools
  • Data Analysis: data cleaning, Advanced statistical methods, data visualization, predictive modeling, reporting
  • Statistical Software: R, Python, SQL
  • Microsoft Office Suite: Excel (advanced), Word, PowerPoint

Projects

Obesity Level Prediction

Developed and evaluated four machine learning models (Logistic Regression, Decision Trees, Random Forest, and KNN) to predict obesity levels from lifestyle and demographic data, achieving up to \(94.5%\) accuracy.

Github link

Education

Bachelor of Science (Hons) Applied Statistics with Computing

Moi University - 2025

Certifications

  • STATA Analytical Program (Distinction) - Moi University 2019