ML Engineer · Data Scientist · East Africa

Freddy
Nyanda

I build predictive ML systems and data products that help teams increase retention, grow revenue, and deliver actionable insights at scale across East Africa.

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Models deployed

200M+

Data processed

12+

Projects shipped

Years experience

Python Expert

AI Solutions

About Me

Building ML that moves East Africa forward

I am an ML Engineer & Data Scientist with 4+ years of experience developing predictive and prescriptive machine learning solutions across mobile money, telecom, and research environments.

My expertise lies in building robust models for customer segmentation, churn prediction, and revenue forecasting. I have hands-on experience deploying LightGBM and XGBoost pipelines into production and translating complex business challenges into actionable AI-driven insights.

Currently, I work as a Pricing & CVM Analyst at Mixx by Yas (Axian Group), where I leverage data to optimize pricing strategies and enhance customer value across Tanzania.

Experience

4+ Years

Specialization

Machine Learning

Location

Dar es Salaam 🇹🇿

What I enjoy building

End-to-end ML pipelines — data, model, deployment
Customer analytics dashboards & segmentation tools
Forecasting & optimization models for revenue & churn
Production-grade APIs and automated data products
CI/CD pipelines for ML workflows
Real-time financial tracking & prediction systems

Work Experience

Where I've delivered impact

Roles focused on turning data into measurable business outcomes using AI-driven systems.

Jan 2025 – Present

Pricing & CVM Analyst

Mixx by Yas · Axian Group

Develop predictive churn and revenue forecasting models using Python, LightGBM, and XGBoost to support customer value management.
Built 7-segment behavioral customer segmentation model (Super Power User → Passive) to optimize campaign targeting, improve ARPU, and enhance retention.
Maintains ML pipelines, ETL procedures, data cubes, and automated reporting systems on SQL Server.
Built PushPromo daily prediction pipeline and Luku SMS Reminder Engine running in production.
Collaborated with commercial teams to translate business problems into data-driven AI solutions.

Jan 2023 – Dec 2024

Lead Data Scientist

AI4D Research Lab

Led end-to-end ML lifecycle, from data acquisition and EDA to model development, deployment, and monitoring.
Deployed deep learning-based prediction models using TensorFlow/PyTorch for early-risk identification.
Improved model accuracy and stability through systematic feature engineering and tuning.
Worked with multidisciplinary teams to deliver AI-driven insights for operational and policy planning.

Skills & Competencies

Tools I use to build & deploy

A snapshot of the tools and techniques I use to design, build, and deploy data products.

🧠 ML & Analytics

LightGBM XGBoost Scikit-learn Predictive Modeling Customer Segmentation Time Series Feature Engineering EDA

🗄️ Data Platforms

SQL Server Python Pandas NumPy ETL Pipelines Power BI Tableau REST APIs

⚙️ Engineering

Docker GitHub Actions CI/CD FastAPI Streamlit Git Azure AI Linux

Featured Projects

Real-world ML systems

Production ML and data science projects with automated pipelines and business impact.

EA Financial Tracker

Real-time East Africa exchange rates & crypto tracker with ML predictions. Hourly automated data pipeline via GitHub Actions. Streamlit dashboard with live charts and forecasts.

Streamlit LightGBM GitHub Actions FastAPI

Code Live Demo

EA News Sentiment API

NLP-powered sentiment analysis API for East African news. Built with FastAPI and VADER, deployed on Streamlit. Generates ~48 automated commits/day tracking sentiment trends.

FastAPI VADER NLP Streamlit

Code Live Demo

Credit Card Segmentation

Customer segmentation using clustering and dimensionality reduction to identify high-value and at-risk cohorts. Includes interactive dashboards for segment profiling and targeting.

Python Scikit-learn K-Means Pandas

Code Notebook

Customer Churn Prediction

End-to-end churn prediction pipeline using Logistic Regression, Random Forest, and XGBoost on transaction data. Includes model monitoring dashboards and feature importance analysis.

Python XGBoost Scikit-learn Dash