• Coding: Python, JavaScript, OOP, HTML/ CSS, Clean Code, TDD, Git;• Backend: Flask, PostgreSQL, SQLite, 3NF, API development, security fundamentals;• Data Science: Python, Pandas, regression modelling, statistical significance;• Data Eng: Kafka, Spark, ETL pipelines, stream processing, queues, data cleaning;• Visualisation: Matplotlib, Plotly, Dashboarding using Dash, Tableau;• Cloud: Docker, AWS, EC2, Lambda;

Machine-learning based approach to sensing and diagnostics in biomedicine. Using neural networks and Sci-Kit Learn, different machine learning algorithms were trained and tested to determine the types of magnetic particles corresponding to potential cancer cells given large datasets of unique magnetic scans.

I was assigned a task to research and produce a technical report on current developments in nanotechnology and its applications in medicine, along with researching on fusion energy. Most of the research was through secondary sources but in order to obtain the most up to date opinions of leaders in the field, I took the initiative to reach out to academics from Imperial College London and University of Cambridge in physics and engineering. This developed my communication skills and built my confidence in working with people of all levels of seniority.

At my workplace, representing the brand is key. This means doing whatever it takes for the delivery of products or services to meet the expectations of the clients, along with identifying and solving problems quickly to the client’s satisfaction. The clients I deal with vary, therefore my approach to any situation that I am in is never the same.