Ezra K.

Key Skills

Software

Other

Top Subject Matter

Machine learning model development

deep learning (signal recognition)

computer vision

Top Data Types

Image

Top Task Types

Fine-tuning

Freelancer Overview

Data Science Intern (Python Developer) — Kenya Power and Lighting Company (KPLC). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Master of Science, University of the West of England (UWE) (2025) and Data Science Certificate, Moringa School (2024). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Fine-tuning, Computer Programming, and Coding.

Labeling Experience

Python Developer / Data Science Trainee — Moringa School

Other

Built and validated deep learning and computer vision models as part of research-oriented AI development, including end-to-end training, validation, and deployment cycles. Implemented classification, object detection, and NLP/image classification approaches to improve model accuracy and efficiency. Containerised and version-controlled AI applications to support reproducible experimentation and deployment. • Developed deep learning computer vision models using PyTorch, TensorFlow, Keras, and OpenCV. • Implemented NLP and image classification models achieving over 80% accuracy. • Built end-to-end data pipelines in Jupyter on Linux for ingestion, preprocessing, training, and deployment. • Containerised applications with Docker and managed workflows with Git for reproducible research.

2024 - 2024

Data Science Intern (Python Developer) — Kenya Power and Lighting Company (KPLC)

OtherFine-tuning

Developed and deployed Python-based machine learning models and data pipelines, contributing to AI training workflows from ingestion through processing to deployment. Worked with large-scale datasets to support model training, validation, and operational readiness. Used deep learning for signal pattern recognition and computer vision tasks, enabling downstream classification and detection use cases. • Built ML pipelines leveraging NumPy, Pandas, and scikit-learn for large-scale processing. • Trained deep learning models in TensorFlow for signal pattern recognition. • Applied computer vision using OpenCV to reduce defect detection errors. • Processed >1TB datasets with Apache Spark/PySpark and optimized pipeline performance.

2023 - 2023

Education

Work History