About Me

I’m Nicholas Misawo, an Applied AI & ML Data Scientist with a passion for building cloud-native, end-to-end, risk-based AI and data systems. My work focuses on turning complex data challenges into actionable insights and production-ready solutions.

Professional Focus

My expertise lies at the intersection of several key areas:

Applied AI & Machine Learning

I specialize in developing machine learning solutions that go beyond academic exercises to solve real-world problems. This includes:

  • Predictive modeling and forecasting
  • Natural language processing
  • Computer vision applications
  • Recommendation systems
  • Time series analysis

Cloud-Native Architecture

I build systems designed for the cloud from the ground up:

  • Scalable microservices architecture
  • Containerization and orchestration
  • Serverless computing
  • Cloud-based data lakes and warehouses
  • Infrastructure as Code (IaC)

End-to-End Solutions

I believe in owning the entire ML lifecycle:

  • Data collection and preprocessing
  • Feature engineering
  • Model training and validation
  • Deployment and serving
  • Monitoring and maintenance
  • Continuous improvement

Risk-Based Approach

Responsible AI is at the core of my work:

  • Model interpretability and explainability
  • Bias detection and mitigation
  • Ethical AI considerations
  • Privacy-preserving techniques
  • Robust validation and testing

Technical Skills

Programming Languages: Python, R, SQL, Bash, JavaScript

Machine Learning: Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM

Data Engineering: Apache Spark, Airflow, Kafka, ETL pipelines

Cloud Platforms: AWS, Azure, Google Cloud Platform

MLOps & DevOps: Docker, Kubernetes, CI/CD, MLflow, DVC

Data Visualization: Matplotlib, Seaborn, Plotly, Tableau, Power BI

Databases: PostgreSQL, MongoDB, Redis, Elasticsearch

Philosophy

I believe that the best data science work happens when:

  • Technical excellence meets business understanding
  • Innovation is balanced with reliability
  • Complexity is simplified without losing rigor
  • Systems are built with users in mind
  • Continuous learning is part of the process

Beyond Work

When I’m not coding or analyzing data, I enjoy:

  • Contributing to open-source projects
  • Writing technical blog posts
  • Exploring new technologies and frameworks
  • Staying updated with the latest AI research
  • Mentoring aspiring data scientists

Let’s Connect

I’m always open to discussing new opportunities, collaborations, or just chatting about AI and data science. Feel free to reach out through GitHub, LinkedIn, or the contact form.