Email: contact@davidbates.me ⋄ Location: London, UK
Professional Summary
MSci Physics graduate from Imperial College London with a strong academic foundation in mathematics, statistics, and numerical modelling. Experienced in applying machine learning, data science, and simulation techniques to complex real-world datasets. Developed and validated predictive models across research and commercial contexts using Python (NumPy, Pandas, Scikit-learn, PyTorch) and SQL. Delivered high-performing models, including a predictive sports analytics pipeline (ROC AUC > 0.74 on 41,000+ matches) and statistical climate models, both demonstrating technical rigour and interpretability. Passionate about applying rigorous analytical methods to drive quantitative insights in a data-rich environment.
Education
MSci Physics — Imperial College London
Upper Second Class Honours, Graduated June 2025
- First-Class grades in final two years; 86% in Master’s thesis.
- Master’s Project: Quantified the climate impact of aircraft contrails using statistical modelling in Python.
- Completed modules in Data Science & Machine Learning, numerical methods, and Monte Carlo simulation.
- Technical exposure to neural networks (Scikit-learn, PyTorch), supervised classification, and algorithmic modelling.
- Built a functional 3D object scanner as part of group systems engineering coursework.
A Levels — Glyn Sixth Form
- 4 A*s — Maths, Further Maths, Physics, Chemistry
Professional Experience
Data Analyst — Pro-Force Ltd
June 2021 – Present
- Automated data reporting pipelines for 100+ commercial clients using Python, SQL, and Excel VBA.
- Reduced reporting time by 90%, delivering consistent output for time-sensitive reporting.
- Built anomaly detection tools for identifying operational outliers and efficiency losses.
- Regularly delivered analytical support under pressure using self-built tooling and queries.
Projects
The Interaction of Aircraft Contrails and Existing Cirrus Cloud — Master’s Project
May 2024 – April 2025
- Conducted statistical analysis of aircraft contrail and cirrus cloud interactions.
- Modelled spatial data using NumPy and Pandas; ensured reproducibility and robustness with Git.
DeepCue: Snooker Prediction Algorithm — Machine Learning Project
April 2025 – Present
- Built a predictive model to forecast match winners using real-world snooker data.
- Achieved ROC AUC > 0.74 across 41,000+ samples using XGBoost and PyTorch.
- End-to-end workflow: feature engineering, hyperparameter tuning, evaluation (AUC, F1, ROC).
NFL Performance Prediction Model — Sports Analytics Project
August 2025 – Present
- Developed a custom Elo-based rating system with dynamic home-field advantage, quarterback/offense/defense splits, and rolling performance metrics.
- Integrated weather, coaching, and betting odds into Random Forest and XGBoost models to forecast game outcomes and assess betting market efficiency.
Leadership
Chair & Secretary — Imperial American Football Society
- Led 40-member team; managed scheduling, recruitment, and budgeting.
- Developed strategic thinking and leadership in a competitive environment.
Skills Summary
Tools: Python, SQL, Excel (VBA), Pandas, NumPy, Scikit-learn, XGBoost, PyTorch, Power BI, Git
Techniques: Statistical Modelling, Classification, Predictive Modelling, Monte Carlo, Optimisation, Cross-Validation
Mathematics: Probability, Linear Algebra, Statistics, Numerical Methods