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 startup, combining AI, computational biology 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.
We’re looking for a Scientific Programmer to help develop our data driven drug repurposing workflow. You’ll work closely with people from other disciplines across the company who are passionate about making a difference for rare disease patients, allowing you to see the impact of your work first-hand.
You’ll be joining a team of computational biologists, AI data scientists, software engineers and pharmacologists to develop the next-generation, AI-enabled drug discovery workflow. You should be experienced in algorithm development, applying machine learning methods to big data, and have a passion for developing robust computational workflows.
Your activities will include:
- Developing scalable drug and disease-matching algorithms by coding scientific notions of how a drug and disease are connected through our workflows
- Provisioning computational pipelines to enable our pharmacologists and data scientists to discover new repurposing candidates
- Improving the success of clinical trials by implementing statistical methods to identify responder patients
- Maintaining our translational medicine computational workflows
Our ideal candidate would be:
- A graduate in relevant bioinformatics, molecular informatics, mathematical or engineering discipline with at least three years experience developing solutions for biotech or pharma
- An expert scientific programmer in Python and one or more of R, Matlab, C++
- An expert user of domain-specific developer kits SciPy, scikit-learn, BioConductor, RDKit
- Deeply interested in extracting novel insight from Pharma Big Data
- Keen to apply modern machine learning methods, statistics and deep learning to develop treatments and improve the outlook for rare disease patients