“You can never understand one language until you understand at least two”
- Geoffrey Willans
It’s a common problem in drug development. Biologists and bioinformaticians don’t understand each other.
We tackle this from day 1 at CytoReason. Our biologists, bioinformaticians, data scientists and engineers sit together in the same room and work on the same projects towards the same goal. After a while they start speaking the same language, complementing each other’s work, and building something big together.
As a Cyto you’ll work with a multidisciplinary on the industry’s most burning questions. Along the way, you’ll help the world’s top pharma companies make groundbreaking discoveries and bring novel therapies to the patients who most need them.
Bioinformatics Scientist – Science Operations
About The Position
You will be joining a multi-disciplinary team of software engineers, bioinformaticians, and biologists to tackle the most burning questions of the pharmaceutical industry using cutting-edge data. You will focus on deciphering disease mechanisms, identifying biomarkers of drug response, identifying novel drug targets, and predicting combinations. All are based on human clinical and molecular data empowered by the company disease model. This role is customer-facing. You will be working with the best pharma companies in the world to bring novel therapies to patients.
- Apply your strong analytical capabilities and a solid understanding of biology to lead the analytical component of our collaborations
- Work closely with Immunologists and Senior Bioinformaticians to explore the data, build and execute analysis plans and deliver biological insights
- Work with our core team to develop and implement algorithms to push our technology even further
- Masters in computer science, Computational Biology, or related disciplines
- Good understanding of statistics and bioinformatics methods
- Experience in R
- Strong communication skills with fluency in English
- Team player, positive and driven
- Attention to details and fast learner
- Experience in omics data analysis preferably in the field of machine learning
- Experience in working with public biological databases, methods and tools
- Experience in developing analysis methodologies
- Experience analyzing diverse data types (e.g. transcriptomics, single-cell analysis)
- Background in Immunology
- Public speaking experience