I am currently a gardener 🪴 in the tech space.
I was a Senior Data Scientist at Dyson where I’ve helped critical decision-making for ~10 Dyson products including supporting the 5-year Home strategy plan. I’ve also brought an estimate of 4m+ people into the Dyson comms funnel through Dyson’s first-ever Global Air Quality Research project and various Dyson Air Quality backpack projects. I was also the tech lead for the data science team in the Home Business Unit and have consulted on mathematical modelling, general data science and data acquisition across various different teams within Dyson.
Prior to work, I studied Mathematics at King’s College London, and then Computer Science at Keble College, University of Oxford.
When I’m not writing code, I like to explore new hobbies. Most recently, I picked up cycling and running. I enjoy good acoustic music and am always down for a game of table tennis, poker or chess.
My tech stack has changed over the years, with some technologies I haven’t used for quite a while now. I’ve done a good chunk of web dev, a bit of iOS mobile dev and a lot of research and applied machine learning. Here are some of them:
- Programming languages: Python, R, C/C++ (prior experience), Swift (prior experience), SQL
- Data analysis: SQL, PySpark, PyData stack, Plotly, NetworkX, Google BigQuery, AWS Redshift
- ML & stats modelling: scikit-learn, statsmodels, scipy, PyMC3, PyTorch, Jax
- Web & dashboard: HTML/CSS/JavaScript, ReactJS, Flask, Streamlit, Tableau
- Deployment: Git, Docker, Google Cloud (GCP) stack
- Others: LaTeX
I’ve basically worked with almost the entire GCP stack including BigQuery, DataProc, Pub/Sub, App Engine, Vertex AI, among others.
