The Role Rheos Medicines is looking for an exceptional individual to lead our Computational Biology and Translational Data Sciences effort at Rheos Medicines. This role is ideal for a highly collaborative individual that will be responsible to lead, develop and execute a transformative data and analytics strategy to accelerate target discovery and drug development in immuno-metabolism. The successful candidate will work effectively in a highly cross-disciplinary environment and will coordinate with leadership across the company, lead development of departmental strategies, and champion the Rheos translational data science platform to both internal and external audiences. Responsibilities Lead the creation of a compelling vision and strategy for the Rheos’s Computational Biology and Data Sciences Department that integrates with the overall scientific strategy and goals of the company, with an emphasis on developing Rheos’s translational and precision medicine capabilities Manage people, timelines, and deliverable's for the Computational Biology and Data Sciences Department; support the technological and development of Rheos’s Computational Biology Scientists, keeping the team at the cutting edge of data science Work cross-functionally with leaders of other functions within the company (which will require knowledge of basic cellular and molecular biology, as well as other areas listed below) Continue the design, development, and implementation of Rheos’s end-to-end computational and data analytics platform Establish and oversee external analysis projects with collaborators and contract research organizations Present Rheos’s Data Science externally at scientific meetings, advisory board meetings, CRO's, and other settings as required Contribute to the authoring of study reports, data science components of clinical protocols, patent applications, regulatory submissions, and peer-reviewed publications Contribute to the preparation of documentation required for regulatory submissions including INDs, especially pertaining to GLP for clinical and omics data analyses Requirements Ph.D. in a quantitative discipline or comparable experience: Bioinformatics, Statistics, Applied Mathematics, Computer Science, Biophysics, Computational Biology, Physics, Engineering or Machine Learning A minimum of 12-15 years of biotechnology or pharmaceutical industry experience post-PhD in Bioinformatics, Computational Biology, or a related basic science field, with 10+ years in discovery research and/or translational development. Experience in leading a high-performance team of computational biologists and data scientists within the framework of a cross-functional, matrixed operating structure History of rigorous scientific contributions through both successful contributions to drug discovery as well as high-impact peer-reviewed publications A history of demonstrating innovation Strong experience and understanding of data and analytics applications in drug discovery, especially the transition from discovery research to the clinic Deep knowledge of bioinformatic pipeline development and implementation across different languages (R, Python, Matlab) to direct computational biology and bioinformatics work using a modern arsenal of computational and machine learning tools, including familiarity with cloud-based infrastructure AWS & GCP) Knowledge of clinical biomarker discovery, development, and analysis; understanding of research approaches for understanding drug mechanism Familiarity/exposure to GLP, assay validation methodologies, and data science aspects that apply to clinical laboratory studies in support of regulatory submissions is desirable A working familiarity with human cellular immunology and autoimmune diseases. Working Knowledge of cellular metabolism, a plus. Appetite to develop metabolism-based biomarkers, essential. Appetite to develop metabolism-based biomarkers, essential Aptitude for people management, especially the cultivation and maintenance of team and company culture Strong and effective communicator across all levels of the organization Can-do attitude with effective teamwork and collaboration skills
