2025 Graduate | MMORSE First-Class
Quantitative Research
Machine Learning
Systematic Trading
Data Science
Python & C++ Development
Algorithm Design
Applied Mathematics
Alpha Research
2025 Graduate | MMORSE First-Class
Quantitative Research
Machine Learning
Systematic Trading
Data Science
Python & C++ Development
Algorithm Design
Applied Mathematics
Alpha Research
First-class MMORSE graduate (University of Southampton 2025) with a passion for transforming mathematics into practical insights. I design and test systematic trading strategies, apply machine learning to large and noisy datasets, and use Python, C++ and advanced statistics to solve complex quantitative problems.
Through projects in derivatives pricing, market microstructure, volatility modelling and predictive analytics, I have built a strong foundation in quantitative research, algorithmic trading and data science. I enjoy taking ideas from theory to execution and developing models that deliver measurable impact in both financial markets and machine learning applications.
I am motivated to grow as a quantitative researcher, trader and data scientist, using mathematics, coding and problem-solving to extract signal from noise, manage risk and build scalable, innovative solutions.
Near-term: Join a research-driven environment where I can apply mathematics, statistics, and machine learning to large datasets — whether in financial markets, technology, or data science — developing models and analytical tools that support data-driven decision making.
Long-term: Progress to a leadership role in quantitative research, leading teams that combine mathematics, programming, and machine learning to deliver robust, innovative, and real-world solutions.
Completed a rigorous four-year programme combining mathematical modelling, statistical inference, and financial economics.
Third-year project: A Blackjack-Inspired Approach to Portfolio Optimization — applied linear and quadratic programming to design risk-aware capital allocation strategies.
Master’s dissertation: An Analysis of Volatility Models in Monte Carlo Simulations — integrated GARCH, Markov Chains, and deep learning to price options with dynamic volatility estimates.
I develop Python analytics and automated pipelines used by the trading
desk to analyse liquidity provider performance, optimise routing,
and monitor execution quality across FX markets.
My work involves designing production data pipelines, building trader
monitoring tools, and analysing market microstructure data to improve
pricing stability and inventory management.
Explored multiple aspects of the multi-currency global payments business, including operations, finance, compliance, and product development. Granted autonomy to identify company needs and deliver solutions, from Excel templates to Python automation.
Contributed to fintech product improvements, blending analytical, technical, and business insight.
Performance led to an offer of part-time continuation during final year of study.
Streamlined affiliate marketing workflows by building Python automation for reporting and process management. Delivered time savings and reduced manual errors across recurring tasks.
Worked consecutive night shifts for 10 weeks to self-fund living costs. Sustained 40+ hours per week, developing resilience, stamina, and rigorous time management. Demonstrated financial independence and goal-driven discipline by covering all expenses without external support.
© 2023 Corey Pritchard
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