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Senior Scientist, Drug Discovery

1 week ago


London, Greater London, United Kingdom Recursion Full time

We are seeking a talented Senior Scientist, Drug Discovery to join our team at Recursion. In this role, you will be responsible for developing and implementing cutting-edge computational methods to analyze and integrate matched human clinical and molecular data in a high-level programming language such as Python or R.

The estimated salary for this role is £110,000 - £140,000 per year, depending on experience.

As a key member of our portfolio-facing data science team, you will be at the forefront of reimagining drug discovery from first principles using our massive data and compute capabilities. You will integrate matched molecular and clinical data from oncology patients with layers of our platform data (phenomics, transcriptomics, proteomics, etc) to validate the link between target and disease biology and propose clinical development opportunities.

You will partner with machine learning and computational scientists from across the organization to develop, deploy, and scale novel approaches to patient selection and indication expansion that match the medicines we are creating to the patients who need them.

  1. Evaluate new data sources, develop and scale new analysis methods, and drive key program decisions with the ultimate goal of bringing life-changing therapies to patients at unprecedented speed.
  2. Deliver biological insights on therapeutic candidates and disease biology from the analysis of high dimensional (phenomics, transcriptomics, proteomics, patient-derived) datasets that support clinical development planning.
  3. Present data analysis to decision makers and stakeholders in a clear and compelling way that drives toward getting medicines to patients.
  4. Industrialize analysis approaches to not only solve for the current project, but also to accelerate future projects and scale the impact that we can have.
  5. Collaborate cross-functionally with our data science, platform, ML and clinical teams to further advance our ability to interpret and translate large-scale data assets into therapeutic programs.