Kieran
Why koralli?
Working for Koralli had immediate appeal because it takes an approach to consulting that feels not only well suited, but increasingly necessary, to the problems many organisations now face. Among these, a core challenge remains around how information is processed, structured and made sense of. Advances in information retrieval and AI have raised both the expectations and demands of what organisations should be able to do with the knowledge they hold, while also highlighting how difficult it can be to make use of this at scale.
AI has enormous potential to reduce costs, improve productivity and change how decisions are made, yet its value remains fundamentally dependent on the quality of the underlying data, and on the systems and behaviours that produce it. At Koralli, I am excited to learn how such systems are best built and sustained in practice, and how these technologies will interact at scale with existing practices around data structure and organisational design.
Just as these problems are often multifaceted, dynamic and context dependent, so too must the teams be that work on them. Koralli’s approach is built around these principles, which is what makes it, for me, a compelling environment in which to learn how complex organisational problems are understood and solved in the face of rapid technological change.
My Skills
My main skills are in statistical modelling, machine learning and applied data analysis. I have experience working with complex data, building and interpreting models, and using tools such as Python, R and SQL to answer practical questions. I also have experience with the supporting work that makes analysis possible, including data cleaning, database structure, pipelines and the communication of technical results to wider audiences.
My Expertise
My expertise is mainly in the application of statistical modelling to complex real-world problems, mostly in the life sciences and usually in settings where the data is noisy and the underlying systems are hard to fully model. I have an undergraduate degree in mathematics and statistics, which had a general focus on applied mathematical modelling, and I have since developed this through work in industrial, academic and consulting settings. I have an MSc in Health Data Analytics and Machine Learning from Imperial College London, which provided valuable training in extracting useful information from high-dimensional data. Through this, I developed experience in statistical inference, machine learning and the use of generative deep learning models for biological problems. I am now a DPhil student in Statistics at the University of Oxford, working on ways to perform Bayesian inference more efficiently for use in problems within epidemiology and computational biology.
My Experience
Prior to joining Koralli, I interned as a statistician in an industrial life sciences setting at Regeneron, where I applied statistical methods to understand and improve real-world manufacturing and measurement processes. I later joined Accenture, where I worked as a data science analyst on projects involving database design, data modelling and business analytics. This gave me experience in how data needs to be structured, documented and maintained so that it can be trusted and used effectively across an organisation. During my MSc, I completed my thesis with a startup where I developed an understanding of how AI may be used for scientific discovery through work on a generative model capable of designing therapeutic peptides used to treat antimicrobial resistance. In my current DPhil, I am continuing to develop skills with simulation methods, probabilistic programming and probabilistic machine learning. Collectively, this has given me experience working across different stages of applied data work, from database structure and data modelling to statistical inference, machine learning and the communication of results in business settings.