Princeton, NJ30+ days ago
D. in a relevant quantitative field (i.e. Computational Biology, Biostatistics, Statistics, Computer Science, etc.) and 1+ years of academic/industry experience or Master's Degree in a relevant quantitative field and 3+ 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 are preferred • 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 • 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. 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 • 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 • Responsible for collaborating with cross-functional teams, including but not limited to clinicians, data scientists, translational medicine scientists, statisticians, and IT professionals • Manage and coordinate resources to produce quality deliverables within timelines for competing priorities.