About the Job
The Statistical Scientist is responsible for implementing analysis plans that support methodological research for clinical decision support (CDS) systems leveraging comprehensive genomic profiling for biomarker-driven personalization of cancer care plans. In this position, the Statistical Scientist evaluates new, cutting-edge methods for the analysis of clinical outcomes data, particularly real-world clinicogenomic data. The position requires exceptional critical thinking, programming ability, and enthusiasm for staying at the forefront of a rapidly changing area of research.
• Implement and refine novel statistical techniques and/or innovative trial designs as developed by members of the Decision Sciences group.
• Design, conduct, and summarize simulation studies to evaluate new statistical techniques and innovative trial designs.
• Analyze and develop visualizations for Real World Data (RWD) using state of the art causal inference methods.
• Collaborate with colleagues in Clinical Development, Cancer Genomics, and Data Science to maximize impact of scientific publications and CDS applications.
• Communicate results within Foundation Medicine and to external stakeholders through scientific publications, conference presentations, and internal reports.
• Other duties as assigned.
• Master’s Degree in statistics or another field with a significant quantitative focus, such as biostatistics, epidemiology, or a related discipline
• Previous experience in a statistically-oriented role
• PhD in statistics or another field with a significant quantitative focus, such as biostatistics, epidemiology, or a related discipline
• 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
• Experience building data-driven tools and/or dashboards
• Experience running simulations or analyses in a high-performance computing environment
• 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
• Solid scientific understanding of cancer genetics and genomics, with demonstrated competency in clinical research
• Strong programming skills with an emphasis on reproducible research (R preferred)
• Excellent verbal and written communication skills, specifically in the areas of presentation and writing
• Technical proficiency, creativity, and a growth mindset
• Ability to understand the limitations of various data sources
• Ability to manage and prioritize multiple projects simultaneously, including both long-term and short-term projects
• Ability to explain complex technical details in clear language
• 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.