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Senior Software Engineer
Cambridge, MA

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Job Description

Job Description

The Getz Lab in the Broad Institute's Cancer Program is looking for a full stack software engineer to assist in the development of data portals supporting the lab's participation in a number of large collaborative research projects, including an IBM partnership studying drug resistance in cancer treatments and NCI's Center for Cancer Genomics' Genomic Data Analysis Network (GDAN). The data portals will provide shared access to project data (data warehousing) and dynamic visualization tools that may be used to explore relationships across data sets. The portals will be built and deployed on the Google Cloud Platform and in some cases will provide analysis services tailored to the portal's research focus. Analysis services will be supported through pipelines running on FireCloud, the Broad Institute's google-cloud-based platform for running genomic data analysis pipelines on large cohorts of data.

Responsibilities

* Work in a collaborative setting with a small team of developers and computational biologists to develop multiple project portals


* Data model design and implementation


* RESTful API design and implementation


* Client software design and implementation, including dynamic data visualizations that allow users to interactively explore associations between different data types (e.g., treatment history and clonal composition)


* Deployment, Operational Support, and Maintenance (note that we deploy portals and web apps on Google App Engine and thus leverage Google Cloud Services for most operational tasks)



No medical, genomics, or scientific background is required, just an enthusiasm to contribute to advances in the scientific understanding and treatment of cancer, and an ability to write exceptional software in an environment that is highly responsive to the applications' user communities.

Due to the small size of the development team (2-3 software engineers) and close proximity of the user community, we employ "lightweight" processes for managing our development projects. While borrowing from agile practices, project management overhead is kept at a minimum. The pace of development is rapid, and software developers have the satisfaction of seeing their software used by lab peers and outside collaborators.

We expect the holder of this position to exercise sound software engineering practices and judgement: (i) designing code for extensibility, but focusing development on immediate deliverables; (ii) writing code that is self-documented and readily comprehensible, with initial implementations focusing on functionality and correctness (performance and scalability addressed, when necessary, in later refactorings).

Qualifications

* BS or MS in Computer Science or related field, with at least 5+ years of software development experience


* Expertise in web development, relational database systems, cloud infrastructure and software engineering best practices


* Experience working with genomic data or in the field of bioinformatics is a plus, but not a requirement


* Fluency in Python and JavaScript


* Familiarity with statistics and R is a plus, but not required.


* Excellent oral and written communication skills. The position will work in a small team of existing members of the Getz Lab. Interfacing with project collaborators in-person and on-line is a key aspect of this position.



Lab Overview

The Getz Lab has established itself as a world leader in the development and application of computational tools for the analysis cancer genomes. The lab specializes in cancer genome analyses which include: (i) Characterizing the cancer Genome, (ii) Identifying cancer-associated genes and pathways, and (iii) Characterizing the heterogenity and clonal evolution of cancer.

Characterizing the cancer genome

Cancer is a disease of the genome that is driven by a combination of possible germline risk-alleles together with a set of "driver" somatic mutations that are acquired during the clonal expansion of increasingly fitter clones. In order to generate a comprehensive list of all germline and somatic events that occurred during life and the development of the cancer, the lab develops and applies highly sensitive and specific tools for detecting different types of mutations in massively-parallel sequencing data. The volume, noise and complexity of these data require developing computational tools using state-of-the-art statistical and machine learning approaches to extract the signal from the noise.

Identifying cancer-associated genes and pathways

Next, the detected oncogenic events across a cohort of samples are analyzed, searching for genes/ pathways, as well as non-coding variants, that show significant signals of positive selection. To that end, we construct a statistical model of the background mutational processes and then detect genes that deviate from it. As part of constructing the models, we study and infer the mutational processes that affected the samples (carcinogens, defects in repair mechanisms, etc.) and their timing.

We have developed tools for detecting significantly gained or lost genes in cancer and genes with increased density or irregular patterns of mutations. Our work demonstrated the importance of modeling the heterogeneity of these models across patients, sequence contexts and the genome, when searching for cancer genes.

Heterogeneity and clonal evolution of cancer

Cancer samples are heterogeneous, containing a mixture of normal cells and cancer cells that often represent multiple subclones. We developed and continue to develop tools for characterizing the heterogeneity of cancer samples using copy-number and mutation data measured on bulk samples and now also analyzing the genomic material in individual cells. Using these tools, we can infer which mutations are clonal or sub-clonal, as well as estimate the number of subclones and their distribution over space and time. Correlating these analyses with clinical data, we can gain insite into the development of resistance during the course of treatment. We are now working to introduce these concepts to clinical trials and eventually clinical care.

Collaborative Science

Members of the Getz Lab work closely with clinical researchers and in large collaborative projects sponsored by NIH (e.g., TCGA) and charitable initiatives (e.g, Stand Up to Cancer, Chan Zuckerberg Initiative) and industry partners. These collaborative efforts are significantly enhanced by the creation of data portals for the sharing of experimental data, analysis results and tools.

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