Ref: 111_1612504219

Computational Biologist

USA, Massachusetts

Job description

Computational Biologist

111_1612504219

Job Description

The candidate will work on data generated from secondary/tertiary analysis pipelines and use AI/ML, probabilistic programming, and other statistical genomics approaches to analyze various large-scale omics data sets to better understand disease etiology, identify novel drug targets, and discover biomarkers for use in precision medicine.

Role & Responsibilities

* data generated from secondary/tertiary analysis pipelines and use AI/ML,
* probabilistic programming, and other statistical genomics approaches to analyze various large-scale omics data sets to better understand disease etiology,
* identify novel drug targets,
* discover biomarkers for use in precision medicine.

Skills & Qualifications

* Sc. or Ph.D. in Engineering, Computational Statistics, Computer Science, Biostatistics, Bioinformatics, or related field with a minimum of 2-years of related industry and/or academic experience
* Experience in machine learning, deep learning, statistical methodology, predictive modeling and algorithm development
* Minimum of 2 years of industry and/or academic research laboratory management experience
* Familiar with NGS data analysis, using common bioinformatics tools (BWA, STAR, Picard, GATK etc.), and knowledge of publicly available genomics databases (i.e. ENCODE, GEO, TCGA, CCLA)
* Advanced programming skills with fluency in at least Python and/or R, with extensive experience using modern machine learning and deep learning libraries (TensorFlow, PyTorch, Edward, sklearn, caret, etc.)
* Proven ability to design and code production grade machine AI/ML applications, along with a strong ability to visualize 'big data'
* Ability to work on high-performance computing system and manage cloud computing environments (e.g. AWS) with experience working with GPUs
* Strong communication and presentation skills with the ability to translate and communicate results to individuals of diverse backgrounds

Perferred Qualifications

* D. with postdoctoral training in Engineering, Computational Statistics, Computer Science, Biostatistics, Bioinformatics, or another related field with a minimum of 5-years of related industry and/or academic experience
* Understanding of modern genomics analysis including RNA-seq, single cell RNA-seq, DNA methylation, variant analysis, etc.
* Development and application of digital pathology and natural language processing algorithms
* Working knowledge of biology (oncology, immunology, autoimmunity, etc.) and target identification
* Up-to-date knowledge of the fast-moving AI/ML literature

Benefits

* Full