• Data Scientist I

    Job ID
    14935
    Type
    Regular Full-Time
    Company
    Fred Hutchinson Cancer Research Center
    Location
    US-WA-Seattle
    Category
    Biostatistics, Bioinformatics and Computational Biology
  • Overview

    Cures Start Here. At Fred Hutchinson Cancer Research Center, home to three Nobel laureates, interdisciplinary teams of world-renowned scientists seek new and innovative ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases. Fred Hutch’s pioneering work in bone marrow transplantation led to the development of immunotherapy, which harnesses the power of the immune system to treat cancer. An independent, nonprofit research institute based in Seattle, Fred Hutch houses the nation’s first cancer prevention research program, as well as the clinical coordinating center of the Women’s Health Initiative and the international headquarters of the HIV Vaccine Trials Network. Careers Start Here.

     

    The Paulovich Lab at Fred Hutch is an interdisciplinary team whose mission is to develop and implement tools for protein quantification to enable precision medicine via patient selection and characterization of novel therapeutic targets. The lab has a major focus on mass spectrometry (MS)-based proteomics (both untargeted and multiple reaction monitoring), as well as integrating proteomic data with genomic and metabolomic data. Our group is highly collaborative, participating in national and international consortia in which teams of scientists, statisticians, and clinicians work together to solve clinical or biological problems to impact human health.

     

    This is a terrific opportunity within the Paulovich lab for a data scientist to work closely with our interdisciplinary team of chemists, biologists, clinicians, and statisticians to leverage multi-omic datasets to understand and predict human cancer responses to therapies. We are looking for a highly motivated, interactive, Bioinformatics/ Computational Biologist to join our proteomics team to help drive translational projects forward towards clinical applications by performing analysis and interpretation of multidimensional “-omics” datasets (DNA, RNA, protein, metabolite, and clinical phenotype) from human tumors and preclinical models. The candidate will participate in the development of data pipelines, plan and conduct analyses, interpret results, design and implement data visualizations, and help prepare figures and text for funding proposals, reports, and publications. The ideal candidate should have the ability to work independently as one hub in an interdisciplinary team.

    Responsibilities

    • Apply data analysis tools and methods to enable the integration and management of large and complex “omics” datasets
    • Interpret data analyses results and write methods and results for publications and reports
    • Collaborate with statisticians, chemists, biologists, and clinicians on consortium projects
    • Research and evaluate new methods and algorithms for integrating into our existing pathway and network analysis tools
    • Perform other job-related duties as assigned

    Qualifications

    Required

    • Ph.D., M.A., or M.S. candidates with degrees in both data science-related disciplines and biology-related disciplines (e.g. a computational biology, bioinformatics, computer science, biostatistics, or mathematics degree, plus a cancer biology, molecular biology, or biochemistry degree) 
    • Minimum 2 years of hands-on biological data science experience (especially with proteomics, RNA sequencing, next generation sequencing datasets)
    • Understanding of molecular biology, cancer biology, and an ability to contemplate the biological implications of results from multi-omic datasets
    • Strong programming skills (e.g. C/C++, R and Python/Perl/Java), with knowledge of the appropriate tools and libraries for working with biological data, including best software development practices (e.g. design, testing, documentation, code review)
    • Experience in pathway and network analysis as demonstrated by peer-reviewed publications and/or software packages
    • Familiarity with pathway and network databases and analysis algorithms
    • Programming in UNIX/LINUX environment
    • Excellent presentation skills and written/verbal communication skills
    • Ability to multi-task and prioritize tasks in a deadline-driven environment
    • Intellectually curious with innovative and creative problem-solving skills
    • Motivated, self-starting, detailed-oriented, organized
    • Ability to learn new technologies at a fast pace
    • Ability to lead data analysis activities independently and with feedback from the group


    Preferred

    • Experience with data visualization tools (e.g. D3.js, Vega, Google Charts, etc.)
    • Familiarity with biomedical ontologies
    • Familiarity with dimensionality reduction, regression models, machine learning and/or cloud infrastructure for scalable scientific computing
    • Experience with graph databases
    • Knowledge of SQL, JSON, and XML
    • Knowledge of statistics and machine learning algorithms

    Our Commitment to Diversity

    We are proud to be an Equal Employment Opportunity (EEO) and Vietnam Era Veterans Readjustment Assistance Act (VEVRAA) Employer. We are committed to cultivating a workplace in which diverse perspectives and experiences are welcomed and respected. We do not discriminate on the basis of race, color, religion, creed, ancestry, national origin, sex, age, disability (physical or mental), marital or veteran status, genetic information, sexual orientation, gender identity, political ideology, or membership in any other legally protected class. We are an Affirmative Action employer. We encourage individuals with diverse backgrounds to apply and desire priority referrals of protected veterans. If due to a disability you need assistance/and or a reasonable accommodation during the application or recruiting process, please send a request to our Employee Services Center at escmail@fredhutch.org or by calling 206-667-4700.

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