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Statistical Scientist

  • Boston, Massachusetts, United States
  • Full Time

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.

Key Responsibilities

• 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.


Basic Qualifications

• 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

Preferred Qualifications

• 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

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Internal applicants, please use your FMI email address.

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