ABOUT THE JOB

The Biostatistician is responsible for contributing and implementing analyses plans to support the design of clinical decision support systems that leverage comprehensive genomic profiling to personalize therapeutic recommendations. In this position, the incumbent performs statistical analyses of clinicogenomic data with the goal of connecting patients to therapies that are most promising based on the tumor’s molecular profile. The position requires exceptional analytical skills, solid scientific background, a high level of professionalism, and enthusiasm for staying at the forefront of a rapidly changing area of research.

Key Responsibilities:

• Implement new statistical techniques on clinical outcome data to support personalized healthcare initiatives in oncology on the basis of molecular profiles.
• Contribute to the writing of internal reports and scientific publications in the areas of methods development and clinical research.
• Solicit feedback on studies and drafted items from relevant stakeholders as appropriate.
• Communicate study results within Foundation Medicine and to external stakeholders.
• Collaborate with Regulatory Affairs, Payor and Data Science teams on biostatistics projects.
• Analyze Real World Data (RWD) in the context of evaluating efficacy and safety.
• Other duties as assigned.

QUALIFICATIONS

Basic Qualifications:

• Master’s Degree in Statistics or another field with a significant quantitative focus.
• Previous experience in a statistically-oriented role.
• Experience with programming  statistical software and working with various database structures, including fluency in one or several scripting languages (Python, Perl) and/or programming languages (R, SAS).

Preferred Qualifications:

• PhD in Statistics or a related field with significant quantitative focus.
• 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) 

• Interpretable machine learning 

• Stan probabilistic programming language
• Next-Generation Sequencing  

• Strong programming skills.
• Capacity for independent thinking and ability to make decisions based upon sound principles.
• 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.
• Solid scientific understanding of cancer genetics and genomics, with demonstrated competency in clinical research.
• Ability to explain complex technical details in clear language.
• Excellent attention to detail and an obsession with quality.
• Understanding of HIPAA and importance of privacy of patient data.