Staff Computational Biologist
2 weeks ago
As part of Recursion's internal pipeline team, you will be at the forefront of reimagining drug discovery from first principles using Recursion’s massive data capabilities. You will be the primary computational lead on one or more internal drug/clinical programs with the freedom and support to collaboratively define and execute a research program to bring promising discoveries from early-stage hit-identification to pre-clinical development. Working alongside world-class biologists, medicinal chemists, engineers, data scientists and ML scientists, you'll design experiments, develop new methods, analyze and interpret results and inform key program decisions with the ultimate goal of bringing life-changing therapeutics to patients.
to accelerate programs (primarily in oncology).
Architect analyses and experiment cascades to collect and combine diverse data that inform statistically-sound decisions weighing risks and benefits.
Present and discuss data with decision makers and stakeholders in a clear and compelling way that drives toward getting medicines to patients.
Industrialize approaches and analyses to not only solve for the current project, but also to accelerate future projects and scale the impact that we can have.
Collaborate cross-functionally with Recursion’s data science and ML teams to further advance Recursion’s ability to interpret and translate large-scale phenomics data into therapeutic programs.
We are building an interdisciplinary group centered on machine learning and inference-based discovery of drug programs from first principles. Close collaboration between computational biologists across multiple therapeutic areas (primarily oncology) and with data scientists on the core build teams provides a strong network of feedback and support in setting timelines, refining ideas, automating repetitive workflows, piloting, productionalizing and more. Our group is a bold, agile, diverse collective of biological, computational, drug discovery, and machine learning scientists deeply focused on the singular goal of bringing new therapeutics to patients at an accelerated pace. You will collaborate extensively with data scientists, chemists, engineers, commercial strategists, and biologists throughout the organization to advance milestones and provide insights to further advance Recursion’s capabilities. Essential attributes to this role include a bold, execution-first attitude and passion for deploying rigorous science to bring life-changing therapies to patients.
PhD in a relevant field (computational biology, bioinformatics, statistics, quantitative pharmacology, cancer/cell biology, etc.) with 3+ years of experience applying professional competencies to fundamental problems in oncology or drug discovery
Experience applying computational methods (including probabilistic, statistical, and/or machine learning techniques) to analyze complex biological and/or human clinical data.
Fluency and experience working with high-level programming language such as Python or R for complex data analysis.
General understanding of small molecule drug discovery and pharmacology.
Efficiency in advancing drug-discovery projects, carrying and prioritizing among multiple projects and efficiently advancing projects to proof of concept.
Experience in efficiently advancing drug program projects from proof of concept to preclinical Industry oncology drug discovery experience preferred.
Ability to clearly explain computational analyses and methodologies to cross-functional and non-technical teams.
Exceptional data visualization skills through creation of plots, documentation and presentations.
Experience analyzing data from imaging- and/or genomics-based modalities.
Training on Recursion’s culture and values and our Drug Discovery & Development processes to provide insight to our unique approach and strategy for success
Opportunity to attend conferences and workshops in Oncology and / or Computational Biology
Multiple opportunities for leadership and mentorship training as well as working with cross-functional teams
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