Senior Associate Data Scientist II

How you’ll make an impact:

Partner with biologists, clinicians, and other data scientists to manage, analyze, and visualize immunological and Next Generation Sequencing data generated internally and externally!

About the team:

You'd be working on our amazing Data & Analytics team under our Research division!

How you’ll spend your time:

Develop and maintain Data Science’s computational production pipelines, data repositories, and visualization/analysis applications to support teams in Research, Clinical, and Manufacturing across multiple stages of the drug development pipeline – from in silico identification of novel targets by mining large public databases (e.g. The Cancer Genome Atlas), to supporting Investigative New Drug packages by detailed analysis of potency enhancements, to deriving correlative insights from translational bioassay data generated as part of internal clinical trials. Use analytical and computational efforts to support and impact efficacy and safety of future gene therapy products. Position reports into Cambridge, MA office; however, telecommuting from a home office is allowed.

Who you are and what you’ve accomplished:

Requirements: Requires a Master’s degree in Bioengineering, Bioinformatics, Computational Biology, Computer Science, Data Science, or a directly related field, plus four years of experience working as a data scientist or bioinformatician in the biotechnology or pharmaceutical industry. Experience may be gained concurrently and must include:

Three (3) years in each of the following:
1. Building and maintenance of R Shiny interactive visualization applications for genomic, immunological, and correlative datasets
2. Operation, optimization, and development of computational workflows for genomic analysis
3. Data mining from genomic tumor databases including The Cancer Genome Atlas (TCGA) and normal tissue databases including The Genotype-Tissue Expression (GTEx) to identify and contextualize novel therapeutic targets in oncology

One (1) year in each of the following:
4. Data cleaning and Extract, Transfer, Load (ETL) of correlative data for relational databases
5. Data processing, integration, analysis, and visualization of single cell RNA and VDJ sequencing data
6. Gene pathway and molecular signatures analysis to support interpretation of correlative datasets
7. Mentoring junior team members on genomics data analysis and visualization and computation workflows