Bioinformatics Scientist

CytoReason is developing the first cell-centered computational model of human disease. With the company’s proprietary database and AI-led platform, pharma and biotech companies minimize the need for animal trials and make human trials more focused and accurate. To date, six of the world’s top ten pharma companies, including Pfizer, use CytoReason’s technology to reach data-driven decisions, and develop new drugs in a fraction of the time and cost.

Role Summary:

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.

Responsibilities:

·      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

Requirements:

·      Ph.D. 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

Advantages:

·      Experience in using machine learning methods

·      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