Data Scientist | ML & AI Practitioner
I turn complex data into actionable business insights and intelligent solutions. I specialize in building end-to-end systems—from data ingestion and feature engineering to modeling, forecasting, experimentation, and deployment—that help businesses make decisions with confidence.
- Programming & Data Analysis: Python, SQL, Pandas, NumPy
- Visualization & BI: Tableau, Plotly, Streamlit, Seaborn, Quicksight
- Machine Learning & AI: Scikit-learn, TensorFlow, Keras
- Databases & Big Data: MySQL, PostgreSQL, BigQuery
- Other Tools: Git, Jupyter Notebook, PyCharm, Google Colab
Specialties: Predictive modeling, time series forecasting, A/B testing, causal inference, feature engineering, AI integration, MLOps (learning)
AI-powered analytics platform delivering automated insights, interactive dashboards, and template-based workflows to accelerate business decisions.
Tech Stack: Python, FastAPI, Next.js, Groq LLM, PostgreSQL
Decision-focused A/B testing framework combining frequentist and Bayesian analysis to guide statistically defensible product and business decisions.
Highlights: Sample size estimation, posterior probability calculations, actionable reporting.
Time series forecasting system providing actionable sales predictions for inventory, staffing, and revenue planning. Uses Holt-Winters smoothing and interactive dashboards.
Highlights: Trend & seasonality analysis, business KPI visualization, scenario-based forecasts.
Most ML projects are descriptive. I focus on decision-oriented data science—making statistical and machine learning results interpretable and actionable for business stakeholders.
- LinkedIn: Emmanuel Nwanguma
- GitHub: Emart29
- Email: nwangumaemmanuel29@gmail.com
I build systems and analyses that turn data into decisions, ensuring insights are reliable, measurable, and tied to business outcomes.