Christopher Letton | Junior Data Scientist

Christopher Letton

Junior Data Scientist

Based in Leeds, UK

About

I’m a data science enthusiast currently completing an MSc in Precision Medicine: Genomic Data Science at the University of Leeds. My passion for data-driven discovery was first sparked during my undergraduate studies and deepened through hands-on experience in a data science bootcamp. I’m particularly motivated by projects that apply analytics to improve lives—whether in healthcare, research, or public service. I’m now looking to apply my growing expertise in a collaborative, forward-thinking environment that values continuous learning and impact. With a strong foundation in programming, statistical modelling, and genomics, I bring both technical capability and a proactive mindset—ready to contribute to real-world challenges from day one.

Skills

Projects

Lymphoma microarray data analysis

I used the lymphoma microarray data for this analysis. The dataset was preprocessed and normalised to ensure data quality and reliability. The analysis included T-test and Limma Analysis. The final results are presented as a ranked list of significant genes.

Mechanisms of actions predictions

I used data provided by a cell-based assay designed to capture gene expression and cell viability levels. Based on the patterns in this data, I created a model to predict a drug's mechanism of action (MoA).

Clustering of new markets

I performed k-means to help with the clustering of a new market. An automobile company identified behaviour of a new market is similar to their existing market. The old market segmented all customers into 4 groups which was proven to be effective. This needs to be done for the new market.

Heart Failure Prediction

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. I created an artificial neural network to predict mortality by heart failure.

Experience

Jan 2023 - Mar

Data Manager at Rubicon Bridge

I supported in the acquisition, cleansing, storing, and the meticulous organisation of extensive datasets. This laid the foundation for conducting in-depth data analysis, enabling the creation of insightful reports and dynamic presentations. My proficiency in managing data not only ensured accuracy but also facilitated seamless accessibility, contributing to the overall efficiency of data-driven decision-making processes within the organization.

Education

2018 - 2022

BSc Hons Genetics, University of Nottingham

Computer Modelling in Science: Introduction – 76% -Constructed simple mathematical models from a set of biological and environmental processes. -Wrote computer programmes to simulate a mathematical model and draw graphs of its outputs. -Critically evaluated mathematical models and their simulation results in the context of relevant challenges in the biological and environmental sciences. -Critically evaluated the use and results of suitable computer algorithms and programs in the context of relevant challenges in the biological and environmental sciences.

Jan 2024 - April 2024

Data Science Bootcamp

Learnt fundamental business and technical skills, focused on interpreting requirements, managing stakeholders, and continually improving proficiency in Python and SQL Learnt about Relational Databases, Data Visualisation and the Data Analytics process. Learnt Supervised and Unsupervised Machine Learning, Neural networks and different types of regression. Covered the topics of Big Data, API's and Data Science within the Cloud.

Sep 2024- Sep 2025

MSc Precision Medicine: Genomic Data Science, University of Leeds

This interdisciplinary programme equipped me with core computational and analytical skills essential for interpreting large biomedical datasets in the context of precision medicine. I trained in R, Python, statistical modelling, machine learning, and specialised techniques such as RNASeq analysis, bioinformatics, and genetic epidemiology. I also gained insight into the biological and technological foundations of genomic data and its clinical application across diseases like cancer and autoimmune conditions. I worked directly with real-world datasets from active research, culminating in a substantial genomics-based research project. The course emphasised real-world relevance through NHS and industry case studies, predictive analytics, and ethical data use. I also benefited from interdiscip