Robert John is a Managing Director and Google Cloud Certified Professional Data Engineer with 8 years of experience transforming software engineering foundations into production-ready data platforms and ML pipelines. Based in Lagos, he has led data teams and architecture at fintech and SaaS companies, built terabyte-scale warehouses, and optimized cloud migrations and containerized workloads for reliability and cost. He combines hands-on model development—recently training NLP sentiment models on Nigerian tweets and news—with strategic ecosystem building, now driving ChipMango’s continent-wide microelectronics training and partnerships. An active contributor to data-science learning projects on GitHub, his notebooks for PacktWorkshops demonstrate practical exercises in feature engineering and model building. Known for bridging technical delivery and external engagement, he mentors multidisciplinary teams and tailors learning content for regional impact. His background in mathematics and a Udacity nanodegree in data visualization underpin a pragmatic, data-driven approach to storytelling and productization.
8 years of coding experience
11 years of employment as a software developer
SSCE High School/Secondary Diplomas and Certificates, SSCE High School/Secondary Diplomas and Certificates at Zamani College, Kaduna
Nanodegree Data Visualization, Nanodegree Data Visualization at Udacity
Bachelor of Technology (B.Tech.) Mathematics and Computer Science, Bachelor of Technology (B.Tech.) Mathematics and Computer Science at Federal University of Technology Minna
A New, Interactive Approach to Learning Data Science
Role in this project:
Data Scientist
Contributions:101 commits, 54 pushes in 2 months
Contributions summary:Robert's commits primarily focused on implementing machine learning exercises within a data science workshop repository. The commits added Python code, specifically Jupyter Notebooks, that involved data preparation, feature engineering, and model building using libraries like pandas, scikit-learn, and potentially others. Each commit introduced a new exercise related to a specific machine learning task within a structured, chapter-based learning environment, implying a focus on model development and data analysis.
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