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"When I was still in college and working at RTI as an intern, I knew it was somewhere that I wanted to work full-time one day. RTI holds its employees to the highest standards, which allows for continuous growth both professionally and personally. I love being able to walk around campus and be immersed in a community of knowledgeable, hard-working innovators who all have a primary goal of improving the human condition." Lily Duchesneau Financial Analyst- SSES Strategic Pricing Group
"It is a pleasure to be part of a company that has such a positive outreach and works to improve the human condition. With a great reputation in the local community and abroad, it is easy to feel proud of the products and services produced by RTI. I like the variation of my role and that I get to work with a wide variety of people across the globe. I enjoy working at RTI because it is a place that demands excellence while still allowing a healthy work-life balance." Tierra Vazquez Sr. Administrative Assistant-International Education
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Social Sciences Research
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SSES EHS
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1905U Requisition #
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RTI’s staff in the Center for Genomics in Public Health and Medicine conduct research in genetic diseases, genomic and environmental interactions in disease development, and evaluation of genetic and genomic testing used in a variety of diseases.  We are seeking a mid-level researcher with a strong quantitative background to aid in developing, implementing, and testing scientific programs to simulate and/or analyze genetic data.  In this role, the candidate is expected to carry out statistics and bioinformatics analyses to support epidemiologists and geneticists, including but not limited to data manipulation, quality control, algorithm implementation, and pipeline development.  This position is based in our Research Triangle Park, NC, Atanta, GA, Washington, DC, or Rockville, MD offices.

Responsibilities:
• Carry out statistical analysis of genomics and genetics data.
• Develop efficient computational methods to implement solutions to above problems.
• Implement analysis techniques in software which can be used in research and product development and which can be handed over to commercial software developers for integration into commercial product.
• Help design, code, test, debug, modify, analyze and document applications and make recommendations for system enhancements.



Qualifications:
  • Master’s degree with 3 years of experience, PhD with no work experience in biostatistics, statistics, bioinformatics, genetics, epidemiology or related health field.
  • At least 3 years of experience using at least one scripting language (eg. Python, Perl, bash), and at least one statistical programming language (eg. SAS, R, Stata, M-Plus) on personal computers and workstations operating in Windows, MAC, and Linux/UNIX environments, and high performance computing cluster.  Experience with C++ and Java a plus but not required.
  • Experience in analyzing data from a variety of high throughput biological screens (i.e. multi-omic data) such as metagenomic, metabolomic, epigenetic, transcriptomic, and/or genomic data.
  • Strong oral and written communications skills and interactions with scientific investigators are required.
  • Ability to work independently with minimal or no supervisor.
  • To qualify, applications must be legally authorized to work in the United States and should not require now, or in the future, sponsorship for employment visa status.
 
Desired Qualifications:
  • Experience in running complex statistical or epidemiological applications.  Experience in design and implementation of industrial grade computational pipelines.
  • Knowledge of foundational principles of statistical algorithm and design and performance characterization, machine learning, or optimization techniques.
  • Experience with handling and analysis of large (GB-TB size) datasets.
  • Experience with cloud computing and familiarity with SQL/NoSQL database, graph, wide column and blob databases.
  • Experience with Jupyter Notebook, Hadoop, and other bid data tools.
  • Experience in the analysis of bioinformatics and statistical genetics data using software packages such as PLINK, IMPUTE2, R-Bioconductor, GATK, SAMtools, VCFtools, BWA, Bowtie, etc.
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