JOB PURPOSE:
Support grant-funded biomedical and behavioral research activities for the Neuropsychopharmacology AI/ML Laboratory and related Howard University College of Medicine initiatives by organizing, cleaning, documenting, integrating, and preparing research datasets for analysis.
The position will assist with All of Us data workflows, substance use disorder cohort organization, survey data restructuring, documentation of genetics/genomics variables, quality-control checks, and manuscript- or grant-related deliverables under faculty supervision.
SUPERVISORY AUTHORITY:
This position has no formal supervisory authority. The employee may provide task-based guidance to students, trainees, or junior research assistants on project-specific data organization and documentation workflows as assigned.
NATURE AND SCOPE:
The position operates in a grant-funded academic research environment and supports data-centered research projects involving biomedical, behavioral, survey, clinical, substance use disorder, health disparities, and genetics/genomics data.
Work requires accuracy, confidentiality, reproducibility, and clear documentation. The employee will interact with faculty investigators, research staff, trainees, collaborators, and university administrative personnel, and must follow applicable institutional, grant, IRB, data-use, and privacy requirements.
Assignments may include preparing analysis-ready files, reviewing data dictionaries and survey instruments, documenting genomic and phenotypic variables, resolving participant-count discrepancies, and supporting manuscript preparation.
PRINCIPAL ACCOUNTABILITIES:
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Organize, clean, and prepare survey, phenotypic, behavioral, clinical, substance use disorder, and genetics/genomics datasets for analysis.
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Support All of Us research workflows, including cohort organization, variable identification, data extraction planning, and documentation of analytic datasets.
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Restructure survey data into participant-level analytic formats when needed and assist with combining multiple data types into unified datasets.
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Write and maintain clear definitions for genetic/genomic and phenotype data elements used in assigned research projects.
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Investigate, document, and communicate discrepancies in participant counts, data availability, coding, or cohort definitions.
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Prepare data dictionaries, codebooks, workflow notes, and reproducibility documentation for internal research use.
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Assist with statistical, bioinformatics, or machine-learning preparation tasks, including variable organization and documentation of assumptions or limitations.
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Contribute to manuscript preparation, including methods documentation, data summaries, table or figure support, references, and revision tracking.
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Support grant-related deliverables, reports, presentations, meeting updates, and other research-related duties as assigned.
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Maintain secure and confidential handling of research data in accordance with Howard University policies, grant requirements, IRB requirements, and data-use agreements.
CORE COMPETENCIES:
- Strong attention to detail and commitment to data accuracy.
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Ability to organize complex datasets and document analytic decisions clearly.
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Working knowledge of data cleaning, variable coding, and dataset preparation.
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Ability to communicate data issues clearly to technical and non-technical audiences.
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Commitment to confidentiality, research ethics, and responsible data stewardship.
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Ability to manage multiple tasks and deadlines in a grant-funded research environment. Strong written communication skills for documentation, reports, manuscripts, and presentations.
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Willingness to learn new data platforms, coding workflows, and analytic methods.
MINIMUM REQUIREMENTS:
Bachelor’s degree in biology, neuroscience, psychology, public health, bioinformatics, data science, statistics, computer science, or a related field required; master’s degree preferred. At least one year of experience working with research data, biomedical datasets, survey data, clinical data, genetics/genomics data, or related analytic workflows preferred. Relevant coursework, internships, thesis work, or laboratory experience may be considered. Experience with one or more data-analysis tools or programming environments preferred, such as R, Python, SQL, SAS, SPSS, Stata, Jupyter notebooks, or cloud-based research workspaces. Familiarity with data dictionaries, codebooks, reproducible workflows, and research documentation preferred. Experience or interest in substance use disorder, addiction neuroscience, neuropsychopharmacology, health disparities, machine learning, genetics/genomics, or large-scale biomedical datasets preferred. Must be able to maintain confidentiality and comply with all university, grant, IRB, and data-use requirements.
