About the Job
The Senior Statistical Scientist leads cutting-edge methodological research in key areas to support the use of real world data (RWD) for clinical decision-making and biomarker-driven personalization of cancer care. In this position, the Senior Statistical Scientist proposes, evaluates, and publishes new statistical methods for precision oncology, with a focus on clinical decision support (CDS) leveraging real-world clinicogenomic data. The position requires exceptional critical thinking, solid cross-functional communication, and enthusiasm for staying at the forefront of a rapidly changing area of research.
• Develop and execute a scientific research and publication plan for new statistical and machine learning models in order to build FMI thought leadership in key areas (e.g., innovative designs, RWD, causal inference, machine learning, decision support).
• Manage efforts of junior Statistical Scientists and Decision Scientists to accomplish research tasks.
• Serve as lead statistical consultant and content area expert in cross-functional interactions.
• Collaborate with colleagues in Clinical Development, Cancer Genomics, and Data Science to maximize impact of scientific publications.
• Communicate study results within Foundation Medicine and to external stakeholders through scientific publications and conference presentations.
• Other duties as assigned.
• PhD in statistics or another field with a significant quantitative focus such as biostatistics, epidemiology, or a related discipline, or relevant Master’s Degree and equivalent work experience
• Co-authorship on one or more publications (pre-print or in-progress accepted) that propose and evaluate a new method in the statistics or machine learning literature
• Solid scientific understanding of cancer genetics and genomics, with a demonstrated competency in clinical research
• Strong statistical programming skills, including experience working with various database structures and fluency in one or several scripting languages (e.g., Python) and/or programming languages (e.g., R)
• Experience building data-driven tools and/or dashboards
• Knowledge of, and experience with, one or more of the following:
· Epidemiology, including causal inference methods for observational real world data (RWD) or real world evidence (RWE)
· Bayesian statistics
· Decision theory
· Clinical outcomes research using data from electronic health records (EHR)
· Discovery mechanisms and evidence generation pathways for novel biomarkers and risk scores
· Interpretable machine learning
· Stan probabilistic programming language
• Ability to manage and prioritize multiple projects simultaneously, including both long-term and short-term projects
• Excellent verbal and written communication skills, specifically in the areas of presentation and writing, and the ability to explain complex technical details in clear language
• Ability to understand the limitations of various data sources
• Technical proficiency, creativity, and a growth mindset
• Capacity for independent thinking and ability to make decisions based upon sound principles
• Excellent attention to detail and a passion for quality
• Understanding of HIPAA and importance of privacy of patient data
• Commitment to FMI values: patients, innovation, collaboration, and passion
Internal applicants, please use your FMI email address.