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Data Science with 4+ years in machine learning and data analysis
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Assessing your cultural and operational fit
Data scientist with a proven ability to build robust machine learning models, transform complex datasets into actionable insights, and create compelling data visualizations. Experienced in end-to-end data science project delivery, utilizing tools such as Python, SQL, Tableau, and Power BI. Adept at applying machine learning techniques to optimize business outcomes and reduce operational inefficiencies. Proficient in managing large datasets, conducting statistical analysis, and deploying models in real-world environments. Recognized for reducing carbon footprints and optimizing data processing, contributing to sustainable and data-driven decision making.
Huawei ICT Academy
Huawei Cloud Service
August 1, 2025 – Present
African Centre for Data Science and Analytics - Nairobi
IABAC Certified Data Scientist
August 1, 2024 – Present
Moi University - Eldoret
Bachelor of Science · Statistics
N/A – June 30, 2023
Africa Population and Health Research Centre
Research Statistician
January 1, 2025 – Present
India
Carbon Games – Portugal
Financial and Data Scientist
January 1, 2024 – December 31, 2024
Portugal
East African Cement Company PLC
Statistics Attaché
May 1, 2020 – July 31, 2022
India
TeachWell Project
June 23, 2026 – Present
Analyzed water, health, and sanitation (WASH) datasets from International Rescue Committee to inform policies on WASH in refugee settings in Dadaab and Kakuma in Kenya. Analyzed TeachWell teachers and learner survey to inform policies on ways to handle mental health issues among teachers and come up with the best methods to modify crisis management in refugee settings in Kenya. Drafted implementation research for TeachWell project in Kenya and came up with a viable conceptual framework to inform the implementation research to be able to weigh the benefits of the different implementation strategies.
HIV & Child Mortality Data Analysis – Independent Project
June 23, 2026 – Present
Engineered analyses of WHO & World Bank data (2000–2023) with Python and spatial models to link HIV, poverty, and child mortality in Africa. Mapped under-five/neonatal mortality to spotlight high-burden countries and poverty-related drivers
Predicting Female-Headed Households Below Income Threshold – South Africa
June 23, 2026 – Present
Designed ML models (LightGBM, CatBoost, XGBoost) to predict income-vulnerable female-headed households at ward level. Delivered scalable insights for policymakers to monitor poverty and gendered household dynamics.
Employee Hiring App – Data-Driven Talent Acquisition & Retention
June 23, 2026 – Present
Built an intelligent hiring app leveraging company data and industry trends to guide evidence-based HR decisions. Generated actionable recommendations to optimize recruitment and boost employee retention.
Powering the Future: Predicting Renewable Energy Use
June 23, 2026 – Present
Analyzed three decades of renewable energy data to uncover global and regional consumption trends. Developed predictive models to forecast energy use and support low-carbon transition policies.
Cultural Fit Analysis
The candidate's project portfolio showcases a strong commitment to leveraging data science for social good and environmental impact (e.g., predicting income-vulnerable households, HIV & Child Mortality analysis, renewable energy prediction, carbon emission reduction). This aligns well with organizations that value ethical AI and data-driven solutions for societal challenges. Their experience in diverse sectors (HR, finance, public health, environmental) and international exposure (Carbon Games - Portugal) suggests an open-minded and adaptable individual who can thrive in varied team environments. The emphasis on reproducible research and evidence-to-policy translation indicates a methodical and impactful approach to work.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving, project management, communication, and teamwork skills through their project descriptions and work history. Their experience in leading project teams and delivering data-driven recommendations suggests a proactive and results-oriented approach. The diversity of projects, from HR to environmental policy and public health, indicates adaptability and a broad interest in applying data science to various domains. The ability to work remotely (Carbon Games) also suggests operational flexibility.