Skip to main content

story

My name is Lizzie, and I joined Kubrick in February 2021 on the Machine Learning pathway.

I’ve been on client site for over two and a half years within the financial sector. My role has been in a data engineering team, delivering a range of data science and data engineering projects for both internal reporting/testing and for feeding external stories about the sector in general. I code in both python and SQL and have been responsible for a range of project critical tasks, including devops and creating data pipelines, consulting and presenting to stakeholders, maintaining/improving live projects, upskilling and training junior engineers/data scientists, and mentoring those in other parts of the organisation who wish to retrain in data skills.

One of my most significant projects so far has been around promoting fair decisions for our customers. I began as a data engineer on the team, and then took over directing the project, allocating tasks, and delivering the project’s key goals by the required deadline. The project’s aim was to produce a python-based tool that could test internal data science models for discrimination/bias against any vulnerable groups within our customer base. The data ethics field is cutting edge so along with the data scientists on my project team we had to forge a way through the conceptual thinking to come up with a plan for how we could conduct fairness testing in the real world, in real customer models. I was especially keen for the tool to be both reusable and simple to use, to encourage thorough testing without data scientists needing to research the academic work themselves, and to have one methodology across the organisation. After our first release, we trialled the tool with a few internal teams, presenting at forums to a range of stakeholders, including technical and non-technical parties from across different business areas, and external companies such as the Alan Turing Institute, and the SAS platform team who were all interested in learning from how we were measuring models against fairness criteria. The tool is now a significant point of interest and in demand from many teams, so business-wide we can be ensuring fair outcomes for all customers.

My organisation uses the agile methodology, so a base level understanding in this was really useful in navigating how project teams work together in my first few months on client site. The training we received in python and machine learning was really comprehensive – as soon as I joined the client, I felt well equipped to add value to the project I was placed on, and also gave me an excellent example of how to teach. Since being on site I have had the opportunity of upskilling new graduates, as well as SQL engineers wanting to learn python and the methodology I learnt at Kubrick has been invaluable for this.

Latest insights