Computational Systems Biologist - Drug Matching

Location: Cambridge, UK
Start Date: Immediate preferred
Salary: Competitive compensation package including share options, subject to experience

At Healx, we're finding treatments for the 350 million people that are affected by rare diseases. We work with pharma companies, patient groups and charities to understand clinical unmet need and identify potential treatments for further testing. Healx is a fast-growing Cambridge UK biotech, combining computational methods and deep pharmacology expertise to identify existing drugs that may treat rare diseases, enabling treatments to be found at a fraction of the time and cost of traditional drug discovery.

A unique opportunity has arisen for a motivated computational biologist, bioinformatician, or data scientist to join the Discovery Team and grow Healx’s drug repurposing portfolio. You’ll work alongside a dedicated cross disciplinary team of biocurators, data scientists, engineers and pharmacologists who are all committed to accelerating the development of rare disease treatments, allowing you to see the impact of your work first hand.

Some examples of our projects are:

• applying drug and disease matching algorithms to find novel drug repurposing opportunities for preclinical testing
• optimizing our drug repurposing workflows by investigating new ways of performing drug matching
• elucidating drug and disease mechanisms of action from Omics and other data sources to assist our pharmacologists in establishing an understanding of why a drug may treat a disease

Our ideal candidate:

• a graduate with at least 3 years professional experience in a relevant computational systems biology or informatics discipline.
• a proven track record of applying pathway and network analysis methods to investigate mechanisms of disease
• an expert in the analysis of gene expression data from a variety of sources
• an excellent command of statistical programming languages such as R and Python
• accomplished user of medical and biological databases to seek insight into mechanisms of disease