Relation uses the power of graph ML and recommender system technologies to understand biology and accelerate drug discovery. As a Machine Learning Scientist at Relation, you will work in an interdisciplinary team (including Data Scientists, Data Engineers and experimental scientists) to deliver our strategic priorities in machine learning and to help build our proprietary technologies. This includes contributing to the design and execution of machine learning projects using the latest methods and engineering best practices, combining typical ML methods with modern graph and NLP-based machine learning architectures such as graph convolutional neural networks, graph attention networks and transformers. You will have experience of working in high-performing ML research groups. You will work within our engineering standards and ways of working.

Relation uses the power of graph ML and recommender system technologies to understand biology and accelerate drug discovery. As a Data Scientist at Relation, you will work alone (deriving insights from biological data) and in interdisciplinary teams (including Machine Learning Scientists, Data Engineers and experimental scientists) to deliver our strategic priority - finding new medicines for patients that desperately need them. You will handle a broad range of biological data, from clinical to single cell RNA-seq to genetics. You will derive insights from these datasets using a broad range of analytical tools including visualization, dimension reduction and feature engineering, traditional and novel statistical methods and nonparametric techniques (including machine learning). You will also work closely with colleagues in the machine learning and data engineering groups to support building our proprietary technologies (components of Relation's end-to-end drug discovery engine), and collaborate closely with our experimental scientists to model and interpret data from the Relation labs.