I am currently a data engineer at Lloyds Banking Group 🏦 working on exciting greenfield projects within the bank.
I was a data scientist at Dyson where I’ve helped critical decision-making for ~10 Dyson products including supporting the 5-year Home strategy plan. There, I’ve 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 data science lead in the Home Business Unit and have consulted on mathematical modelling, 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 learn new things. Most recently, I’m learning to speak the Saudi dialect, learning how to play the drums and also aiming to get GCP-certified by the end of March. 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’s my active stack:
- Programming languages: Python. // Prior experiences - R, C, Swift
- Cloud dev: GCP
- Data engineering: Apache Airflow & Google Cloud Composer, SQL, PySpark
- DevOps: Git, Docker, Terraform
- Data analysis: PyData stack, Plotly, NetworkX, Google BigQuery, AWS Redshift
- Machine learning: scikit-learn, statsmodels, scipy, PyMC3, PyTorch, Jax
- Web & dashboard: HTML/CSS/JavaScript, ReactJS, Flask, Streamlit, Tableau
- Others: LaTeX
