Scientific Data Engineer

2 weeks ago

Durham, United States Veear Full time
Deliverables:
  • Facilitate the implementation of software tools to be used by the entire division.
  • Build and maintain a repository of shared code for new and ongoing projects. - Quarterly
  • Contribute to team-science publications that describe new modeling applications. - Yearly
Work Details:
  • Coordinate building bioinformatics infrastructure to ensure easy and meaningful scientific analysis and interpretation of data. 1
  • Collaborate with scientists to design, analyze, manage, and interpret all types of data. 2
  • Work with staff on the planning of experiments, and data analysis for internal and collaborative projects; use bioinformatics expertise to advise and help bench scientists on experimental design and troubleshooting. 3
  • Work with staff on the development and maintenance of bioinformatics tools, scripts, and pipelines for data. 4
  • Collaborate with biologists, statisticians, and/or other bioinformaticians in the design of models summarizing/explaining experimental data. 5
  • Provides programming and troubleshooting support to the Federal Government in the dissemination of research
  • Generate and optimize programs and scripts for the analysis of data; create programs and algorithms and develop computational infrastructure resources for organizing and parsing data from large and complex data.
  • Serve as bioinformatics expert and coordinate with teams of biologists to conduct experimental queries and/or perform portions of studies using complex procedures and techniques common to modern bioinformatics.
  • Records observations and reports results at weekly laboratory meetings data.
  • Performs computations on research data analysis.
  • Evaluates new types of experimental approaches to protocols based on knowledge of scientific literature, available facilities and research needs.
  • Identify new tools and resources for reaching biologically meaningful conclusions.
  • Independently coordinates the training of personnel in the use of scientific software applications, statistical software applications and programmatic software applications.
  • Provide training in and technical support (including product updates and version control) for programs, algorithms, archives, and pipelines generated during the course of this work.
  • Provide ad hoc trainings and hands-on workshops on the use of bioinformatics tools.
  • Initiates interdisciplinary collaborations with other research centers.
  • Work with an interdisciplinary team to apply computational data analysis approaches to make biological discoveries.
  • Deliver at least one presentation per year to audiences outside the government.
  • Attend group meetings; present findings; and author publications resulting from projects.
  • Present analysis results at research conferences and meetings.
  • Present scientific and technical data at scientific conferences.
  • Provide interpretive analyses of data derived from different experimental platforms to generate biological meaning.
  • Works with staff on scientific programming and experimental design.
  • Prepare scientific reports and progress reports; assemble data to prepare tables, graphs, and slides; conduct scientific and program-related information searches; and report results.
  • Provides statistical support/analysis on research data.
  • Devise Client methods of statistical analysis for the collected data.
  • Provides research/service goals in the context of the laboratory's overall mission.
  • Develop and optimize programs and scripts that facilitate organization, integration, and data-mining of large data sets; integrate these models into a framework of best practices.
Minimum Education:
  • Masters
Additional Qualifications:
Certifications & Licenses:
  • Bioinformatics training
Field of Study:
  • Information Sciences
  • Computer Programming and Data Processing
  • Mathematics
Software:
  • Linux
  • Java
  • CSS
  • JavaScript
  • ArcGIS
  • PHP
  • R
  • Bash
  • GitHub
  • Python
Skills:
  • Work with Large Data Sets
  • Pipeline Development
  • Data Quality Control
  • Data Harmonization
  • Algorithm Development
  • Statistical Modeling
  • Data Cleaning
  • Data Analysis
  • Research
  • Clinical and/or Translational Research
  • Machine Learning

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