Senior Computational Biologist, Tooling
Recursion · London, United Kingdom · Data Science
About this role
Recursion is hiring a senior-level Research Scientist in the machine learning function based in London, United Kingdom. The posting calls out experience with Python, Configuration Management, ETL, Machine Learning. Compensation is listed at £86,300–£115,500 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- London, United Kingdom
- Department
- Data Science
More roles at Recursion
Job description
from Recursion careersYour work will change lives. Including your own.
The Team You’ll Join
As part of Recursion's Value Tech Tooling Team, you will be at the forefront of reimagining drug discovery from first principles using our massive data capabilities. You'll work in a cross-functional team of world-class computational biologists and engineers who turn complex analyses and data workflows into stable tools and pipelines that multiply their impact and industrialise the discovery process across a wide array of therapeutic areas. You'll partner with biologists, medicinal chemists, engineers, data scientists, ML scientists, and to design experiments, develop new methods, build scalable data workflows, analyse results, and identify common themes across programs. As our toolbox grows and evolves, it will contribute to a revolution in drug discovery to bring life-changing therapeutics to patients at greater speed and with higher quality, safety and efficacy than ever before.
In this role, you will:
- Synthesize, integrate, and analyse diverse internal and external datasets from computational biology, target discovery, functional genomics, biomarker discovery, and real-world and clinically derived data sources, and identify commonalities to turn into reusable tools and pipelines.
- Build, maintain, and improve robust data pipelines and analytical workflows for complex biological datasets, including omics data, internally generated biological assay data, and external data sources such as Tempus and other real-world datasets.