I have two broad roles that I fulfill at the Bank. In the first place, I am the main architect behind the Bank’s big data user infrastructure and tooling. I also lead projects with various Bank departments to improve their data science processes and practices. A few notable items from my work:

• I have built and maintain an array of web-scrapers which collect data the Bank requires for its operations.
• I have built and maintain many R packages that enable Bank staff to conduct data analysis on the Bank’s big data servers.
• I have built and now administer a custom automation system to allow Bank staff to automate potentially long or intensive data operations.
• I have led work to modernise one of the Bank’s important statistical models.

^{Previous role: Data Analyst (up to Feb 2020)}

The FT’s Data Science team is responsible for creating and productionising statistical and machine learning models for use within the organisation. While at the FT, I was involved in the following initiatives:

• I was at the forefront of developing our Lifetime Value (LTV) modeling, where we estimate how likely each of our customers are to renew their subscriptions.
• As part of our product strategy I undertook a project to attribute value to our various product categories. This involved determining how much monetary value and engagement power is generated by each part of our digital real-estate.
• I have performed scenario modeling on a number of proposed product and commercial changes to determine their potential effect on advertising and subscription revenue.
• I’ve been involved in training other members of staff in how to use R.

I worked in the the Client Referential Data (CRD) team which is responsible for managing client static and reference data.

While at Maitland, I was part of the Instrument and Fund Setup team.

A package to make recurring calendar events a little easier by providing a simple grammar for expressing them using R code. The package includes functions to:

• Represent all kinds of date elements found in the lubridate package: years, months, weeks, days, weekdays, quarters, semesters etc.
  
• Represent dates in terms of their relations to one another, such as the “third Friday of July” or the “last Thursday in August”.
  
• Specify ranges of dates, such as “after the first Monday in July” but “before the last weekday of August”.
  
• Compose the above elements together in arbitrarily complex patterns.