Data scientist with 20+ years of experience modelling complex, noisy systems — originally in astrophysics, now applied to real-world data problems.

Published machine-learning researcher with 20+ years of quantitative modelling experience, now applying those skills to commercial problems.

My work focuses on machine learning, statistical inference and time series analysis, with an emphasis on extracting signal from difficult datasets and supporting decision-making under uncertainty.

Machine Learning

• Classification
• Regression
• Neural Networks

Statistics

• A/B Testing
• Confidence Intervals
• Hypothesis Testing

Forecasting

• ARIMA
• Holt-Winters
• Prophet

Programming

• Python
• SQL
• Git
• C
• IDL
• Unix shell

• Fraud Detection (Machine Learning)

  Developed supervised models to identify rare events in highly imbalanced data, achieving strong precision while maintaining a low alert rate. Focused on threshold optimisation, uncertainty, and real-world trade-offs between detection and operational cost.

• A/B Testing Toolkit (Statistical Inference)

  Built reusable Python tools for comparing groups using confidence intervals and hypothesis testing. Designed to support practical decision-making in experimentation workflows.

• Time Series Forecasting Toolkit

  Created an interactive framework for comparing forecasting models (ARIMA, Holt-Winters, Prophet) with built-in backtesting and error analysis to evaluate real-world performance.

• Deep Learning for Regression

  Implemented neural network models for continuous parameter estimation from high-dimensional data, including full pipelines for preprocessing, training, validation, and uncertainty assessment.

Python (pandas, NumPy, scikit-learn, TensorFlow), statistical modelling, machine learning, time series forecasting, hypothesis testing, data visualisation.