If you are interested in data science, statistics, and real world applications of machine learning, we will probably get along.
I study Mathematics and work as a Quantitative Research Intern analysing large scale datasets to study market behaviour and build predictive models. My goal as Chair is to help make the Data Science Society a place where members can genuinely develop practical data science skills and apply them to real problems.
What have I done?
I currently work as a Quantitative Research Intern analysing more than 400,000 blockchain transactions using Python, Pandas, NumPy, and statistical modelling to study liquidity flows, trading behaviour, and market dynamics.
Alongside this, I lead a quantitative research team developing data driven strategies, guiding analysts through feature engineering, backtesting, and statistical validation of models.
At Imperial’s Mathematics Department, I supervise research students through the Directed Reading Programme, teaching topics such as time series modelling, stochastic processes, and quantitative analysis.
What will I do?
- Launch a structured Data Science learning track covering Python, statistics, machine learning, and data engineering.
- Introduce hands on projects using real datasets so members gain practical experience.
- Run technical workshops on topics such as time series modelling, machine learning pipelines, and data visualisation.
- Organise data science competitions and hackathons to encourage experimentation and collaboration.
- Bring in industry speakers working in data science and AI.
My goal is to make Data Science Society a place where members do not just learn theory, but actually build, analyse, and publish meaningful data science projects.