At Roche, we believe it's urgent to deliver medical solutions right now - even as we develop innovations for the future. We are passionate about transforming patients' lives and we are fearless in both decision and action. And we believe that good business means a better world.
That is why we come to work each day. We commit ourselves to scientific rigor, unassailable ethics, and access to medical innovations for all. We do this today to build a better tomorrow.
In Roche's Pharmaceutical Research and Early Development organisation (pRED), we make transformative medicines for patients in order to tackle some of the world's toughest unmet healthcare needs. At pRED, we are united by our mission to transform science into medicines. Together, we create a culture defined by curiosity, responsibility and humility, where our talented people are empowered and inspired to bring forward extraordinary life-changing innovation at speed. These positions are located in the Predictive Modeling and Data Analytics chapter, a chapter within the Pharmaceutical Sciences function, where Computational Toxicology and Safety is a primary focus area. We closely collaborate with our therapeutic areas and functions to convert hypotheses into innovative therapeutics.
In our team of computational toxicology and safety, we bring together diverse expertise on pre-clinical, clinical and real world data in order to gain a deep understanding of the molecular mechanisms of toxicology findings and safety risks of small and large molecules. As a member of the team you will contribute to our prime responsibility of developing safe and efficacious drugs creating exceptional value for our portfolio and for patients.
You support the prediction and identification of molecule properties, off-target proteins and pathways or biological mechanisms of toxicological findings using high- and low-dimensional data like single cell sequencing, spatial -omics, FACS and molecule centered assays.
You will support establishing (machine learning) models for predicting molecule properties (large and small molecules) like physicochemical properties, immunogenicity and others.
You will help our teams to establish strong links and relationships between preclinical in vitro and in vivo models and findings and patient safety risks (clinical data, real world data) and liabilities.
PhD in cheminformatics, computational biology, bioinformatics, statistics or related fields with experience in an academic or industry setting particularly in the area of safety and / or toxicology.
Proficiency in working with chemoinformatics, bioinformatics and statistical tools and methods as well as a strong background in statistics and/or machine learning
Proficient in Python, R or another programming language
You exhibit a growth mindset. You ask for feedback and act on it. You embrace opportunities to gain new skills and perspectives and provide honest feedback to others to help them grow.
You are able to follow the science. You act on facts and data, not opinions and go where the science and the unmet needs of patients lead you.
You are able to radically simplify and prioritize for impact. You invest your time in things that accelerate progress for patients. You stop doing things that do not.
These positions are located in Basel
You're someone who wants to influence your own development. You are looking for a company where you receive the opportunity to pursue your interests across functions and geographies. Working in a multi-cultural environment motivates you.
Are you ready to apply? We want someone who thinks beyond the job offered - someone who knows that this position can be a rare springboard to many other opportunities at Roche.
Roche embraces diversity and equal opportunity in a serious way. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work will be.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.