Data Scientist

📍 Nairobi, Kenya

I have over three years of experience working with data, helping organizations make informed decisions by extracting actionable insights from raw data and building data products(dashboards,predictive models, recomendation models as well).I have a diverse background across various roles, allowing me to adapt and contribute effectively. My experience spans machine learning, data modeling, analysis, dashboards, and visualization—skills that align well with this role’s demands.As an experienced communicator and collaborator, I thrive in team environments. Beyond work, I’m passionate about farming, hiking, and reading classic literature.

💻 Skilled: Machine & Deep Learning, Statistical & Data Analysis, Data Modelling & Visualization

Technical : Python, R, C++,SQL

Education

Bachelor of Statistics and Information Technology | ALX - Data Science | Udemy

Work Experience

Data Scientist | Upwork Machine Learning Models, Python, SQL Jan 2023 – Present

• Enhanced client process efficiency by 25% through the development and implementation of machine learning models, tailored to optimize specific business objectives.
• Increased model performance by 30% by utilizing advanced machine learning libraries such as TensorFlow and PyTorch, focusing on clustering, training, testing, and evaluation techniques.

Data Verifier Lead | Selistar Africa Data Verification, Quality Assurance, Team Training Oct 2024 – Dec 2024

• Improved data quality by 25% through the implementation of rigorous verification protocols and structured data validation workflows to identify and resolve inconsistencies.
• Trained and supported 10+ data verification specialists, reducing reporting errors by 20% through audits, feedback sessions, and automated error detection.

Machine Learning Intern | Technohacks TensorFlow, Keras, PyTorch, Azure AI Studio June 2024 – Sept 2024

• Achieved a 15% improvement in student performance prediction accuracy by developing and implementing machine learning models using Python and TensorFlow to analyze large datasets and extract insights.
• Boosted model reliability by 20% through collaboration with educators, developers, and data analysts to improve model performance using advanced preprocessing techniques and statistical analysis.

Data Analyst |MEAL Samburu Awareness Action Program SQL, Kobo Collect, Power BI Sept 2023 – Jan 2024

• Enhanced program effectiveness by 30%, measured by improved program outcomes and participant satisfaction, by using data analysis to assess what works and make necessary adjustments.
• Increased transparency in reporting by 40%, measured by clearer and more impactful results presentations, by analyzing data to quantify the outcomes of initiatives and highlight successes and areas for improvement.
• Improved resource allocation efficiency by 20%, measured by reduced waste and increased project output, by analyzing data to determine the most effective use of funds, personnel, and materials.

Supply chain Analyst Intern | Inventory Management | Sendy Logistics Excel, Tableau, Report Writing May 2022 – Sept.2022

• Reduced excess inventory by 8% through the use of Excel forecasting techniques, optimizing inventory levels and minimizing waste.
• Enhanced real-time visibility and decision-making by maintaining Tableau dashboards, providing up-to-date insights into inventory status. Increased process consistency and facilitated knowledge transfer by documenting supply chain procedures.

Projects

Malaria Detection Using Deep Learning

Developed an automated approach for malaria detection by analyzing microscopic blood cell images through deep learning techniques. Leveraged a data-driven strategy to identify parasitized cells, significantly enhancing diagnostic capabilities for early malaria detection.Implemented Convolutional Neural Networks (CNNs) to classify blood cells as infected or uninfected. The system utilized a labeled dataset of cell images, followed by preprocessing steps like image augmentation and normalization to optimize model performance. The model was evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure robustness.

Customer Churn- Machine Learning

Developed a customer churn model for a telecom company using machine learning. Data was cleaned and preprocessed with Pandas and NumPy, and key churn factors like contract length, call duration, and billing methods were explored through Seaborn and Matplotlib. Logistic Regression and Decision Trees were applied, achieving 85% accuracy with Decision Trees. Power BI dashboard to monitor churn in real-time, helping the company implement targeted retention strategies that reduced churn by 15%.

Reward Program Dashboard

I developed a Reward Program Dashboard by cleaning and merging multiple data sources to build a comprehensive dataset, removing duplicates and standardizing formats. I implemented an automated point allocation system that calculates points based on session participation, duration, and mentee engagement with transparent documentation. Finally, I created an interactive Power BI dashboard to visualize key insights, enabling data-driven recommendations that optimized the reward structure and boosted user retention.

KIVA Loans Analysis & Modelling

Analyzed the relationship between poverty levels and microloan distribution in Kenya using Kiva microfinance data. Utilized statistical modeling and geospatial mapping in R to assess loan effectiveness across counties. Identified disparities in loan distribution, highlighting gaps in financial access for rural communities. Provided insights on strategic microfinance allocations to enhance poverty alleviation efforts.