Computational Biologist/Data Scientist,
London
Relation Therapeutics overview
Relation Therapeutics is a TechBio company pioneering recommender systems biology to bring forward new drugs for patients with diseases of high unmet need.
Relation are combining single-cell profiling, human genetics, functional genomics, and end-to-end machine learning to better understand human biology. The company’s ultimate goal is to transform how drug discovery & development is conducted, leading to new medicines for diseases where there is a tremendous need.
Relation is using graph-based recommender system technologies to reveal causal relationships in diseases that until now have been impossible to understand using traditional technologies. Ultimately, Relation’s platform will be capable of identifying which areas of biology to focus on and greatly accelerates discovery research efforts for diseases that have not previously been widely researched.
At Relation we embrace diversity, equality and inclusion and we are committed to building diverse teams. We are an equal opportunities employer and do not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability or age. We strive to create an inclusive interdisciplinary workplace that cultivates innovation through collaboration, empowering and supporting everyone to do their best work and develop to their highest potential.
Opportunity
Joining Relation Therapeutics as a Computational Biologist, you will be an essential member of our collaborative and interdisciplinary team. Working closely with Machine Learning Scientists, Machine Learning Engineers, Data Engineers, and experimental scientists, your role is pivotal in advancing our strategic priorities in computational biology. Your contributions will play a key role in shaping the development of our proprietary technologies.
Role Responsibilities
As a Computational Biologist at Relation, you will actively engage in designing and executing cutting-edge projects that leverage innovative computational biology approaches, to drive data enabled drug development. Based at our wet/dry lab and headquarters in central London, you'll be at the forefront of addressing unique challenges posed by extensive models and vast datasets in the pursuit of revolutionising drug discovery.
Your contributions will be instrumental in unlocking new frontiers in genetics, working with data from proprietary biobanks and clinical trials. Identify key data types, methods, and tools, staying at the forefront of bioinformatics methods in single-cell transcriptomics and genetics. Your technical leadership will be crucial in developing our in-house bioinformatics data infrastructure, ensuring that Relation’s platform remains a leader in data-driven drug discovery.
Professional Qualifications
PhD in bioinformatics, computational biology, computer science, or another quantitative discipline; or equivalent industrial experience.
Demonstrable track record of delivering complex data science-driven projects.
Proficiency in software development using Python, with upwards of 2 years of industrial experience.
Experience with single-cell data or genetics data preferred, or any other biological/health/chemical data.
Familiarity with modern software engineering practices, collaborative tools, and CI/CD.
Desirable experience in either Software Engineering to help develop and maintain code base or Machine Learning.
Desirable Domain Knowledge
Robust understanding of advanced statistical techniques applicable to human biology, particularly in therapeutics development.
In-depth knowledge of statistical human genetics with the ability to creatively apply principles in target identification and validation.
Competency in statistical programming (e.g. Python, R) sufficient to enable large-scale genomic data analysis
Experience working with databases and data types related to target ID, including: pathways, phenotypes, ontologies, molecules, clinical data, etc.
Expertise in handling broad omics data, including but not limited to single-cell transcriptomics, human genetics, and epigenetics data.
Proficiency in data mining and/or management of large datasets.
Familiarity with health records and/or chemical datasets is a bonus.
Experience in one of; Statistics, Genetics or Omics.
Openness to engaging in machine learning projects and a willingness to develop skills in this area as needed.
Personally, you are
Inclusive leader and team player.
Clear communicator.
Driven by impact.
Humble and hungry to learn.
Motivated and curious.
Passionate about making a difference in patients’ lives.
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