MSc Artificial Intelligence • Machine Learning Engineer
Python • SQL • Statistical ML • Model evaluation & deployment
I recently completed an MSc in Artificial Intelligence at Northumbria University, building a strong foundation in machine learning, deep learning, statistical modelling, and time series analysis. I work primarily in Python (PyTorch, TensorFlow, scikit-learn, pandas, NumPy), and am comfortable using SQL and modern data engineering tooling. I focus on building clean, reproducible modelling workflows that translate effectively from experimentation to production.
My interests centre on applying machine learning to complex, real-world problems — particularly those involving structured data, forecasting, optimisation, and decision-making under uncertainty. I'm drawn to machine learning research and engineering roles, and equally to quantitative research where the same rigour applies to financial data.
I bring strong analytical and statistical thinking, a research-oriented mindset, and a practical awareness of how models behave in real environments. I value clarity, reproducibility, and technical precision, and communicate complex ideas clearly to both technical and non-technical audiences.
Combining Deep learning with Explainable AI (XAI) for the prediction of Cardiovascular Disease. Implemented using PyTorch
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Implemented reinforcement learning approaches to train a sensor-equipped F1-Tenth car to race on a track. Extended the baseline with a Human-in-the-Loop approach and compared performance through evaluation.
Preprocessed an unfiltered raw dataset using EDA and outlier-resistant normalisation. Built a DNN in Python using Keras to predict house prices and compared results against a traditional neural network using metrics such as RMSE.
MSc Artificial Intelligence — Merit (69%)
Northumbria University, 2024 – 2025
BSc Computer Science — 2:1 (65%)
Newcastle University, 2021 – 2024
Advanced Machine Learning on Google Cloud
Professional certification
Languages: Python, SQL, Java, JavaScript, HTML, C, C++
ML: PyTorch, scikit-learn, evaluation/validation, XAI
Data: pandas, NumPy, data cleaning, feature engineering, end-to-end pipelines
Engineering: Git, APIs, OOP
Interests: Finance, Machine Learning, Statistics