I joined DMW as a consultant in 2018, coming straight out of academia where I had been researching the evolutionary genetics of echolocation and speciation in bats. Initially I worked at DMW three days a week while I completed writing my doctoral thesis, moving to work full-time after it was submitted. While I decided in the course of my PhD that the academic life didn’t suit me, my love of data, analysis and problem solving combined with the technical skills I had developed through first learning and then teaching bioinformatics made technical consulting an easy fit.
I have a a strong analytical and statistical background, and this hands-on, end-to-end data science history allows me to quickly understand the kinds of data problems faced by our clients. I’m adept at summarising and presenting problems, progress and outcomes to a variety of audiences with different levels of interest or technical competency. While I’m happy to write testing strategies and scope documentation for other members of a team to work from, I also have enough coding experience to get stuck in with testing issues and potential solutions myself. My broad base allows me to rapidly pick up new skills when required.
I have been working as part of a team deploying a data science platform at a multinational energy company. We’ve taken this platform from being a small proof-of-concept up to the point that it is a tool being used across three continents to analyse time-sensitive data to facilitate business-critical decisions. My role has involved developing the platform, expanding its capabilities, upskilling users and helping debug issues that they encounter in its use. We’ve been able to increase the transparency and reproducibility of data science in the organisation, and increase collaboration between data scientists and business analysts.