Computational Biology Co-Op

Flagship Pioneering Co-Op Program•Cambridge, MA

2d•$25 - $35

About The Position

Each day, the lives of more than 2 billion people across the globe are impacted by chronic diseases. Moreover, the economic burden on society from treating chronic diseases is spinning out of control. Today, this dire situation appears unlikely to change as >95% of global healthcare costs are spent on treating rather than preventing chronic diseases. Etiome is a privately held early-stage company that is applying advanced biological and computational platforms to discover breakthroughs in detection of and intervention against the etiologies that drive progression from health to disease. We aim to disrupt the current approach of treating chronic disease after symptoms onset and provide true health care rather than sick care to individuals that are at risk of progressing to disease. Flagship Pioneering is a biotechnology company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture. Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com . Our Team: The computational biology team at Etiome is a small group of scientists with diverse expertise spanning machine learning, image analysis, biology, causal inference, and software development. The team’s main focus is on the development and application of the Temporal Biodynamics platform, which leverages the heterogeneity of scRNA-seq data and patient clinical information to build temporal models of disease. In collaboration with the biology team, we leverage these models to identify novel, stage-specific targets and biomarkers in applicable patient populations. We also close the loop on experimental validation by ingesting, processing, and helping interpret the data from in-house experiments that test our hypotheses. Position Summary: We are seeking a computational biology co-op who is enthusiastic to use a mixture of computational skills and biological knowledge to aid in the identification, ingestion, and analysis of publicly available data for early proof-of-concept research and feasibility testing of our proprietary data platform. The ideal candidate will have a strong background in programming using python, especially in its application to transcriptomic analysis, a strong understanding of high-level concepts in biology, and excellent communication skills.

Requirements

Python programming experience
Strong understanding of general concepts in cellular biology as they apply to drug discovery
Exposure to single cell transcriptomic analysis software and concepts
Experience reading and interpreting results from scientific literature

Nice To Haves

Exposure to machine learning and statistical methods used in computational biology
Experience working with version control/git
Familiarity with R/nextflow
Experience wrangling large-scale publicly available atlas data

Responsibilities

Identify and ingest suitable public datasets for internal use
Leverage existing QC and data analysis pipelines to process data
Optimize and refine analysis based on biological interpretation of results
Document and record version-controlled analysis using git
Present results to the computational biology team
Work with the computational biology team to maintain and improve internal software