Associate Director - Biomarker Data Sciences

Responsibilities Develop and apply novel or existing computational methods for patient segmentation from multimodal clinical and omics datasets for various treatment modalities in partnership with Translational, Clinical and Statistical Scientists. Partner with lead and protocol statisticians in writing, reviewing and executing protocols and statistical analysis plans (SAP) for biomarkers and diagnostics, highlighting the biomarker strategy for clinical drug development. Defend biomarker analysis plans at internal reviews and provide reviews of complex biomarker-related study and analysis plans. Take a lead role on biomarker-related analysis plans and analyses; and revise as needed as data emerges. Coordinate biomarker analyses on datasets from BMS clinical trials and real-world data cohorts. Plan and perform relevant and innovative statistical analyses of high-dimensional (e.g. gene expression, sequencing) data generated by cutting edge technologies. Execute and contribute to the scientific and statistical strategy of drug development, including development of predictive biomarker(s) and precision medicine. Collaborate with biomarker scientists in planning and executing experiments to optimize and validate biomarker assays for clinical trial usage. Develop, implement, and apply state-of-the-art algorithms to address key business problems and drive the implementation of innovative statistical methods in support of biomarker strategy. Formulate, implement, test, and validate predictive models and implement efficient automated processes for producing modeling results at scale. Manage and coordinate resources to produce quality deliverables within timelines for competing priorities. Contribute to internal and external scientific communities, bringing forth innovative approaches to biomarkers and drug development. Develop and advise team members. Effectively communicate the GBDS Mission and Vision in a fashion that generates pride, excitement and commitment within GBDS. Enable a culture of inclusiveness, respect for diversity, compliance with process and allows for the questioning and challenging of others in a respectful and constructive manner. Provide support and leadership to address health authority requests, publications, presentations, and other public releases of information. Requirements Ph.D. in a relevant quantitative field (i.e. Computational Biology, Biostatistics, Statistics, Computer Science, etc.) and 6+ years of academic/industry experience or Master’s Degree in a relevant quantitative field and 8+ years of industry experience. Strong experience in the analysis of data generated by one or more -omics or molecular assays is required. Knowledge of molecular biology, understanding of disease pathways. Strong experience in biomarker data analysis with data generated from clinical trials, or electronic health records. Experience in modeling methods particularly in their application to pharma R&D. Experience in the application of AI/ML, and proficient in SQL, Python, and R and cloud platforms. Experience developing statistical and machine learning models on high dimensional and high throughput data for time to event data and longitudinal outcomes. Perspective in leveraging innovative approaches to expedite drug development and address the complexities of emerging data. Demonstrated ability to lead a team. Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects. Strong problem-solving and collaboration skills, and rigorous and creative thinking. Excellent communication, data presentation, and visualization skills. Capable of establishing strong working relationships across the organization. #J-18808-Ljbffr Jobtailor