Computational Biology Scientist, Prescient Design

Full time contractor position (40h per week), up to 12 months

At Roche, we believe it is critical to deliver medical solutions 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.

Position

The Large Molecule Drug Discovery group within Prescient Design / (MLDD - Machine Learning for Drug Discovery) seeks
exceptional Computational Biologists with background in protein structural biology and design, computational methods (such as machine learning), and project management, a passion for research and technical problem-solving, and a proven ability to implement ideas and apply methods.

The group provides a dynamic and challenging environment for cutting-edge, multidisciplinary research including access to
heterogeneous data sources, close links to top academic institutions around the world, as well as internal Genentech Research and Early Development (gRED) and Pharmaceutical Research and Early Development (pRED) partners and research units. Our mission is to develop and apply methods in designing novel macromolecules. You will work closely with teams in Structural & Computational Biology and Machine Learning to enable end-to-end data workflows for large-scale data ingestion, processing, analysis, and modeling, including our core machine-learning framework. Your responsibilities will include data preparation and cleaning, deploying both internal and external discriminative and generative models, and collaborating closely with and–contributing uniquely to–many different project teams across the company.

Responsibilities:

As a Computational Biology Scientist, Prescient Design, you will:

• Enable cutting-edge research in machine learning and applications to drug discovery, design, and development through the collection, design, and management of data pipelines and infrastructure;
• Execute computational and statistical analysis of single B cell data analysis, antibody structure prediction/analysis, visualisation and presentation of large data sets in support of antibody selection and drug development;
• Manage collaborations between experimental biologists and computational colleagues and bioinformaticians across departments in the organization and drive development and establishment of future antibody discovery processes;
• Collaborate closely with cross-functional teams across both Prescient Design and pRED to support data ingestion and development of dataflows;
• Interface with other teams at gRED & pRED in developing a common data architecture and model and in formalizing best practices;
• Be expected to help develop, manage, and scale data pipelines and infrastructure for analysis and modeling in production;
• Be expected to serve as an expert and resource for multiple, diverse groups at Prescient Design and pRED.
• Reference: -J48mVQ7AzDz0o-k4LBIjK4OBqxK6tCMihllbghxBd4.

Requirements & Qualifications

• B.S., M.S., or Ph.D. in Computer Science, Statistics, Applied Mathematics, Computational Biology, Physics, related technical field, or equivalent practical experience
• Demonstrated experience in molecular design, especially in therapeutic contexts, pushing forward portfolio projects and meeting design objectives
• Strong, demonstrable experience of using computational methods to analyze antibody structure and function or antibody repertoire analysis for antibody engineering and decision support
• At least one year relevant work experience
• Strong programming skills in languages like C++, Python, Java, Scala, or SQL and experience with scientific computing software and scripting languages
• Experience developing and maintaining codebases and software libraries, following industry best practices.
• Intense curiosity about the biology of disease and eagerness to contribute to scientific and computational efforts
• Prior experience or familiarity working with protein sequence and/or protein structure data- would be considered as a plus
• Familiarity with experimental data from biology, immunology or related disciplines would be considered as a plus
• Experience in physics-based modeling tools i.e. molecular dynamics, docking or Rosetta would be considered as a plus
• Public portfolio of computational projects (available on e.g. GitHub) would be considered as a plus
• Familiarity with cloud computing (AWS) would be considered as a plus

Should you answer the above challenges and requirements with YES then this is the right position for you!

Who we are

At Roche, more than 100,000 people across 100 countries are pushing back the frontiers of healthcare. Working together, we’ve become one of the world’s leading research-focused healthcare groups. Our success is built on innovation, curiosity and diversity..

We believe in the power of diversity and inclusion, and strive to identify and create opportunities that enable all people to bring their unique selves to Roche.

Roche is an Equal Opportunity Employer.