ML Engineer


Data Wrangling, Data Streaming, Data Clean-Up, Data Analysis, Business Analysis, Visualization, Dashboards, Feature Engineering, Machine and Deep Learning, Predictive Analytics, Natural Language Processing, Image processing, Time Series Prediction, Web App Deployment, Cloud Services, End to End AI & ML project delivery.

The goal is to create viable digital solutions for real life problems using Data.

Tosin is an accomplished Data and Machine Learning Engineer with a PhD in Engineering. With his proficiency in Python software development and business analysis for digital transformation, he is a highly sought-after professional. He specializes in end-to-end Cloud-native data and machine learning projects that cover a wide range of areas including data engineering, data analysis, data modelling, data visualization, as well as machine and deep learning. Tosin is particularly passionate about promoting the knowledge-Innovation-Enterprise nexus by employing cutting-edge solutions driven by data to solve real-life business problems. His expertise in Data Engineering makes him stand out among other professionals in his field.


AI Applications

Artificial Intelligence (AI) is the use of a combination of Machine Learning and Deep Learning techniques to create models - trained using vast volumes of data, deployed to systems/machines enabled with intelligence that simulates human behaviour or thinking to solve specific problems. AI application is revolutionizing almost every industries helping solve complex problems.

ML & Deep Learning

ML models are algorithms that use statistics to find and apply patterns in data - usually big data. The data could be structured, semi-structured or unstructured. Deep learning is Machine learning on steroids - uses the technique of deep neural network - gives machines enhanced ability to find and amplify, even the smallest patterns in data.  And they both pretty much run the world (not really).

Tools: TensorFlow, PyTorch, Scikit-Learn

EDA & Data Analytics

A crucial aspect of developing any data product. This is a first look at the data to understand its features and most important characteristics. Assessment of the direction and rough size of relationships between different variables as well as the potential data product that can be produced from the raw data. EDA types can either be graphical (Visualisation) or quantitative (Statistical summary) for univariate or multivariate data systems.

Tools: Python, Pandas, Numpy, SQL, Scikit-learn

Data Visualization

An invaluable tool for explaining the significance of rather complex array of findings to people by turning them into images and/or interactive applications. This can empower the viewer or user with greatest number of ideas in the shortest time with the least use of space and printing.

Tools: Plotly Dash, Matplotlib, Seaborn, Tableau, PowerBI


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The goal is to turn data into information, and information into insights.


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Data are becoming the new raw materials of business.


Let's work together!

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