Scientific Data Analyst

IMAGINEEER LLC

6d•Remote

About The Position

In this role, you will provide computational, data science, and bioinformatics support for Office of Research Innovation, Validation, and Application’s (ORIVA) research validation and application programs across D-NICEATM and DAIBR. You will develop and maintain NIH databases, build analytical tools, support computational modeling, and deliver data-driven insights to a multidisciplinary team advancing human-centered biomedical research. This role sits at the intersection of data science, regulatory science, and biomedical research.

Requirements

PhD in bioinformatics, mathematics, statistics, computer science, data science, or related field with 4+ years of relevant experience OR Master's degree in an equivalent field with 4 years of additional experience
Proficiency in at least two programming languages: Python, R, Perl, or equivalent
Experience with bioinformatics tools, databases, and high-throughput data analysis
Experience managing and integrating large biomedical or genomic datasets
Strong background in statistical analysis, data modeling, and computational methods
U.S. citizenship required; ability to obtain Public Trust clearance

Nice To Haves

Expertise in NAMs or computational toxicology
Experience with NIH scientific databases and platforms
Familiarity with in silico modeling, Adverse Outcome Pathway (AOP) frameworks, or ICCVAM/OECD validation standards
Prior NIH and/or HHS contractor experience
Experience with cloud platforms or HPC environments
Peer-reviewed publications in a related field

Responsibilities

Develop and execute analytical workflows to process, analyze, and interpret large-scale biomedical datasets
Apply computational and bioinformatics methods in support of New Approach Methodologies (NAMs) related research objectives
Maintain and enhance NIH databases and tools through data curation, integration, and interoperability support
Build and maintain data pipelines and analytical tools using Python, R, or equivalent languages
Develop and update user support materials, websites, and training resources
Ensure all published materials meet 508-compliance standards
Present complex analytical results clearly to scientific and non-technical audiences
Support data validation, cleansing, and governance per NIH data quality standards