Scientific Data Engineer - Human Computational Neuroscience Employer NIH, National Institute of Neurological Disorders and Stroke, Functional Neurosurgery Section Location Bethesda, Maryland Salary Salary commensurate with experience and qualifications Closing date Dec 14, 2026 View more categories View less categories Sector Government , Hospital , Independent Research Institute , Non-profit Institution / Non-governmental Org. Job Function Data Science, Analytics, & Software Engineering , Research Staff / Technical Director Research Area Cognition , Integrative Physiology & Behavior , Motivation & Emotion , Techniques Position Type Contract Level Mid Level Apply now Save job Click to add the job to your shortlist You need to sign in or create an account to save a job. Send job Job Details Position Description The Functional Neurosurgery Section at the National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), is seeking a Scientific Data Engineer to support our research in human computational neuroscience. Our laboratory studies the neurophysiological basis of human cognition using large-scale high-resolution electrophysiological recordings obtained from patients undergoing neurosurgical evaluation and treatment. Our work combines neuroscience, engineering, signal processing, software development, and quantitative analysis to investigate the neural mechanisms underlying human cognition. These datasets are scientifically valuable, technically complex, and exceptionally large. Individual participants can generate many terabytes of multimodal data, creating substantial challenges in data management, processing, quality control, analysis, and long-term preservation. We are looking for a highly organized and technically skilled individual to take primary responsibility for the laboratory's data infrastructure and computational pipelines. The Data Engineer will maintain and improve systems for storing, backing up, processing, and organizing existing datasets; oversee the ingestion and processing of newly collected data; maintain and improve the laboratory's shared codebase; and work with scientists to develop more robust, efficient, and reproducible approaches for analyzing neurophysiological data. The Data Engineer will work closely with a multidisciplinary team. The position offers substantial opportunities to develop new technical approaches, improve research practices, and contribute to scientific projects according to the candidate's interests and expertise. Key Responsibilities Data Management and Stewardship Maintain and organize the laboratory's existing neurophysiological and behavioral datasets. Manage reliable storage, backup, archival, and retrieval of large datasets. Develop and maintain procedures for data integrity, versioning, metadata, and quality control. Oversee the ingestion, organization, validation, and backup of newly acquired data. Monitor storage resources and anticipate future infrastructure needs. Maintain appropriate safeguards for sensitive human-subject data and patient confidentiality. Develop clear documentation of data organization, processing procedures, and computational infrastructure. Data Processing and Computational Pipelines Maintain and improve existing pipelines for processing large-scale neurophysiological datasets. Automate repetitive processing, validation, and quality-control procedures where appropriate. Monitor pipelines for failures or unexpected results and develop tools for identifying and resolving problems. Improve the efficiency, reliability, reproducibility, and scalability of existing workflows. Develop tools for transforming raw recordings into standardized, analysis-ready datasets. Software Engineering Maintain and improve the laboratory's shared scientific codebase. Refactor and modernize existing research software when appropriate. Establish and promote good software-development practices, including version control, documentation, testing, code review, and reproducible computational environments. Work with laboratory members to integrate newly developed analysis tools into shared computational pipelines. Help ensure that research code developed for individual projects can, when appropriate, become reliable and reusable laboratory infrastructure. Analysis Methods and Scientific Collaboration Collaborate with neuroscientists and quantitative researchers to improve methods for analyzing electrophysiological and behavioral data. Implement, evaluate, and optimize signal-processing and computational analysis methods. Help develop robust and reproducible approaches for analyzing neural time-series data. Evaluate new computational tools and methods and determine when they could improve laboratory workflows. Provide technical guidance to laboratory members on data processing, analysis, software development, and computational best practices. Depending on the candidate's interests and expertise, contribute directly to scientific analyses and methodological development. Qualifications Required Bachelor's, Master's, or doctoral degree in computer science, engineering, computational neuroscience, data science, physics, mathematics, or a related quantitative field. Strong programming skills, particularly in MATLAB. Experience developing and maintaining scientific or data-processing software. Experience working with large or complex datasets. Familiarity with version control and collaborative software-development practices. Strong organizational skills and careful attention to data integrity and reproducibility. Ability to independently troubleshoot technical and computational problems. Strong written and verbal communication skills. Ability to work effectively with researchers from diverse scientific and technical backgrounds. Preferred Experience with electrophysiological, neurophysiological, or other biological time-series data. Experience with signal processing and quantitative analysis of time-series data. Experience designing or maintaining automated data-processing pipelines. Experience managing large-scale storage, backup, or archival systems. Experience with high-performance computing, cloud computing, containers, workflow-management systems, databases, or related data infrastructure. Experience with software testing, continuous integration, documentation, and reproducible research practices. Familiarity with neuroscience and/or human-subjects research. 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