Computational Biologist at Neon Therapeutics
Cambridge, MA, US
We are seeking an innovative, collaborative and enthusiastic computational scientist to work on a variety of high-dimensional bioinformatics projects centered on target identification, drug discovery, and translational research. The individual will integrate clinical and research datasets, including mass spectrometry and next-generation sequencing data. The successful candidate will be one who can develop novel methods, analysis strategies, and tools to filter and distill multi-dimensional datasets to focused hypotheses to drive clinical and discovery research. A critical component of the job will be the close collaboration with multi-disciplinary internal discovery teams and external collaborators. This individual will be a critical thought leader in the company, collaborating closely with discovery and development scientists.


Enhance molecular characterization of tumors for both clinical and research setting by designing and evaluating novel integrative approaches to the analysis of NGS cancer exome and transcriptome data
Correlative analysis of patient outcomes, phenotypes, and immune readouts with diverse high dimensional data types (NGS-based, ) to identify biomarkers and mechanistic hypotheses about response and resistance to cancer vaccines and other therapies
Identify and interpret gene expression signatures related to cancer pathways and immune infiltration
Develop innovative, robust, analysis pipelines that can be applied in research and clinical settings
Drive analysis and idea-generation related to epitope selection for Neon’s cancer vaccine and T-cell based therapies
Identify, develop, manage and contribute to external collaborations and partnerships
Provide guidance on experimental design and data interpretation by liaising with lab scientists
Serve a project team member and a scientific resource for key company programs

Ph.D. in bioinformatics, computational biology, genetics or a related discipline + 3 to 5 years of relevant experience as a scientist in an industry or academic setting with a strong record of publications/patents
Experience developing and optimizing NGS-based software, especially that which relates to custom variant detection (for fusions, microsatellites, etc.) and variant phasing
Experience executing, optimizing, and pipelining GATK tools
Fluency in SAM/BAM file format specifications and the use samtools and pysam to interpret these files and evaluate variant support; familiarity with gene and variant annotation files (GTFs, VCFs)
Technical proficiencies, including:
Use of Linux. Experience in a cloud environment preferred
Familiarity and experience applying statistical fundamentals
Fluency in R and python (strictly required)
Experience integrating diverse NGS and non-NGS data sets to identify cancer drivers, predictive biomarkers, or mechanistic hypotheses
Experience with mining public data sets, such as TCGA, GTEx, and CCLE
Demonstrated ability to formulate and test hypotheses by designing and implementing novel computational approaches
Capacity to prioritize and work independently to complete tasks and advance projects with minimal supervision
Ability to work effectively with internal and external collaborators and multidisciplinary teams composed of scientists and non-scientists to translate emerging research to guide internal programs
Ability to identify partners/consultants to complement internal bioinformatics efforts
Proficient written and verbal communication skills, particularly of complex information and concepts
Creative and innovative thinking; track record of thought leadership
Additional Qualifications

Familiarity with the basics of immunology
Expertise analyzing gene expression signatures, especially as they relate to tumor purity and infiltration of immune cell types
Previous experience implementing neural networks, logistic regression, and other machine learning approaches. Experience using GLMs, keras, or pytorch is preferred.

To apply, please send your resume. In the subject line please note the position for which you are applying.