Preclinical Biostatistician

Position Summary

Genocea Biosciences is a biotechnology company focused on bringing lifesaving personalized cancer vaccines and immunotherapies to patients. Genocea’s revolutionary ATLAS™ platform is a high-throughput bioassay that utilizes a patient’s own immune system to identify T cell antigens that are critical to fighting disease. In the case of cancer, the ATLAS platform identifies which patient-specific tumor neoantigens are optimal for recognition by the patient’s immune system and inclusion in a vaccine or immunotherapy. A Phase 1/2a clinical trial of Genocea’s GEN-009 personalized vaccine is ongoing. Genocea is also developing a broad pipeline of cancer immunotherapy candidates including next-generation vaccines and cellular therapies.

We are seeking an experienced and motivated biostatistician to join our team. The successful candidate will provide significant statistical expertise in preclinical study design and data analysis in vaccine development and discovery research. They will also support statistical analysis of human immunogenicity assays to define positivity and responder criteria. The biostatistician will have the opportunity to work on developing novel statistical methodologies and improve current methodologies. They will also contribute to scientific advances through independent and collaborative research resulting in presentations and publications.

Primary Responsibilities

  • Serve as key biostatistician on preclinical study design and data analysis

  • Work with scientists to improve reproducibility and repeatability of existing assays using rigorous statistical methods

  • Develop and maintain good working relationships with research and clinical scientists, statisticians, computational biologists, and external collaborators as part of a multidisciplinary team.

  • Provide strategic input into the analysis of large and complex data sets from high throughput platforms

  • Maintain and expand expertise in various computing tools to leverage internal and external data sets

  • Application of various statistical approaches and innovative thinking to analyze immunogenicity assays using real and simulated datasets: define positivity and responder criteria, minimizes false positive and negative errors, improve reproducibility

  • Ability to define pros and cons of applying different statistical methods to clinical data.

  • Build out statistical capabilities and propose opportunities for productivity improvements and implementation plans.

  • Proactively seek input and review from other experts within and outside the group on various projects and research activities and share technical information when appropriate.

Required Skills

  • Proficient with statistical modeling and inferential statistics; actively seeks to acquire knowledge

  • Strong basis in statistical concepts and methodologies such as predictive modeling, mixed effects models, multivariate analysis, etc.

  • Exploratory data analysis and data visualization

  • Expertise in design of experiments and power/sample size calculations

  • Sound understanding of assay qualification and validation parameters and methodology, and statistical applications for assay development.

  • Strong statistical programming skills with R and/or python and SAS

  • Excellent communication, presentation and writing skills with an ability to explain complex technical details in simple language.

  • Good knowledge of drug development and FDA regulations pertinent to statistical analysis of assays.

  • Work well in a team as well as independently and be able to take a leadership role regarding statistical elements in various projects.

  • Candidate will need to understand the "language" of biology, immunology and vaccine development

  • Background in immunology and oncology would be a plus

  • Experience in applied Bayesian modeling would be a plus

Education Requirements

  • MS or PhD in Biostatistics, Statistics, Computational Biology or a related field

  • >1 year with PhD (or >3 years with MS) relevant post-graduate academic/industry experience collaborating within a cross-functional research team