Scientific Data Engineer

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