Oct 2021 – Jan 2025 · 3 yrs 3 mos · Greater London, England, United Kingdom

• Successfully delivered, as lead developer in a team of four, two integration projects with the UK and Singapore financial regulatory authorities over 6 months, being responsible for project planning, development and external communication.
• Decreased process execution time from 2.5 hours to 20 minutes and cut infrastructure costs by 50% of two core business services by redesigning them to use distributed computing based on AWS orchestration tools.
• Increased reliability by 95% and performance by 80% of system-critical web bots through the implementation of a novel website navigation and web page state detection algorithm.
• Eliminated noisy neighbour problems and reduced service complexity from O(n) to O(1) by replacing a shared cloud implementation with a dynamically scalable and highly parallelizable architecture.
• Reduced the execution time of .NET lambda functions by 70% by optimising C# code and building a system to detect imminent service demand and dynamically allocate lambda resources.
• Improved UI responsiveness and decreased database usage by over 1 million daily requests by replacing an API polling solution with a websocket notification service for long-running backend operations.
• Mentored five software engineer new hires effectively by catering to their learning & communication styles and delivering value.

Nov 2020 – Jul 2022 · 1 yr 8 mos · Greater London, England, United Kingdom

• Developed a data science pipeline to process socio-economic data about Westminster and enable data exploration in a website.
• Awarded a collaboration with the WCC Strategy & Intelligence team and invited to speak at the WCC 2021 Symposium and at the WCC 2022 Spend it Locally Conference.
• Featured a Python backend for data extraction, cleaning and enrichment; a React web UI; and automatic data (geo)visualisation with D3.js or Google Maps API based on data format, type and aggregation.
• Pioneered a novel approach for estimating demographically-segmented daytime population using Google Maps Places data.

Jun 2020 – Sep 2020 · 3 mos · Greater London, England, United Kingdom

• Automated the scraping and cleaning of financial regulatory data from over 100 data sources of jurisdictions worldwide.
• Reduced detection time of errors by 80% by implementing alert triggers determined by statistical analysis of historical data.

Jun 2017 – Sep 2017 · 3 mos · London Area, United Kingdom

• - Reduced toil by automating the extraction and loading of client data into internal SQL database clusters for processing.