Faith Nyabuti

• Produced monthly reports using advanced Excel spreadsheet functions.
• Utilized data visualization tools to effectively communicate business insights.
• Created various Excel documents to assist with pulling metrics data and presenting information to stakeholders for concise explanations of best placement for needed resources.
• Improved decision-making processes with accurate data analysis and visualization techniques.
• Collected, cleaned, and analyzed large datasets related to patient health records, disease prevalence, and hospital operations using Python (Pandas, NumPy) and SQL for data manipulation and querying.
• Developed dashboards and reports to track key health indicators using Tableau, Power BI, and Python libraries like Matplotlib, Seaborn, and Plotly, providing actionable insights to hospital management and public health stakeholders.
• Conducted statistical analyses using scikit-learn, SciPy, and Statsmodels to identify trends in disease outbreaks and assess the impact of public health interventions.
• Collaborated with healthcare professionals to design and implement data-driven solutions, utilizing ETL pipelines and cloud services like Google Cloud Storage for streamlined data workflows and improved patient outcomes.
• Automated data collection and reporting processes using tools such as Python (BeautifulSoup, Selenium) for web scraping, Excel (VBA, macros), and Apache Airflow, reducing manual workloads and enhancing efficiency.
• Monitored and evaluated public health programs using geospatial analysis with GeoPandas and QGIS, providing recommendations for optimizing program effectiveness and sustainability.
• Ensured compliance with data privacy and security standards, including GDPR and HIPAA, when handling sensitive health information.
• Provided training to staff on data analysis techniques and tools, including Jupyter Notebooks, Excel, and Power BI, fostering a data-driven culture within the hospital.

• Prototyped machine learning applications, leveraging Jupyter Notebooks and Google Colab, and conducted feasibility analyses to quickly determine application viability.
• Developed custom machine learning algorithms tailored to specific industry needs using Python (scikit-learn, TensorFlow, PyTorch), resulting in improved performance and operational efficiency.
• Collaborated with multi-disciplinary product development teams to identify performance improvement opportunities, integrating trained models using APIs and microservices for seamless deployment.
• Composed production-grade code to operationalize machine learning models, utilizing frameworks such as FastAPI, Flask, and Django REST Framework, ensuring scalability and reliability for web-scale consumption.
• Deployed machine learning solutions to cloud platforms, including AWS (SageMaker), Google Cloud AI Platform, and Azure Machine Learning, optimizing resource usage and cost-effectiveness.
• Designed and implemented end-to-end machine learning pipelines using Apache Airflow, MLflow, and Kubeflow, enabling efficient model training, validation, and deployment.
• Utilized NLP tools like spaCy and Hugging Face Transformers to develop language models for digital skill assessment and training.
• Integrated real-time data streams into models using Kafka and Redis, enabling live performance monitoring and predictive analytics.
• Conducted workshops and training sessions to promote digital and AI skilling, focusing on AI ethics, data literacy, and model interpretability, empowering local communities and professionals.
• Advocated for inclusive AI development by collaborating with regional organizations to address Africa-specific digital challenges, ensuring equitable access to AI technologies and skilling resources.

Developed and maintained geospatial databases to support project workflows and ensure data accuracy.
Processed and analyzed satellite and aerial imagery to extract actionable insights for environmental monitoring and urban planning.
Utilized advanced GIS tools and software, including ArcGIS, QGIS, and Google Earth Engine, to conduct spatial analyses and create detailed visualizations.
Conducted remote sensing tasks, including image classification, change detection, and feature extraction, to monitor land use and resource management.
Provided geospatial intelligence for disaster response by identifying vulnerable areas and proposing mitigation strategies.
Designed and implemented custom geospatial scripts using Python and R for data automation and analysis.
Integrated geospatial intelligence with demographic, economic, and environmental data to support decision-making processes.
Coordinated with cross-functional teams to ensure geospatial deliverables met project timelines and quality standards.
Presented geospatial findings and recommendations through detailed reports and interactive dashboards, ensuring clarity for non-technical stakeholders.
Delivered training and mentorship to team members and stakeholders on geospatial tools and methodologies, improving overall team capacity.