Digital Health And Behavior Data Scientist (50697)

GAP SOLUTIONS INC•Baltimore, MD

7d•$1 - $2•Onsite

About The Position

The Digital Health And Behavior Data Scientist will provide advanced computational and data science expertise to support digital health and behavioral research within the NIH Intramural Research Program supporting the National Institute on Drug Abuse (NIDA). This position is responsible for leading multimodal data integration, machine learning and simulation modeling, and supporting human subjects research activities to advance understanding of substance use, treatment outcomes, and recovery. The ideal candidate will demonstrate a strong publication record, including peer-reviewed scientific papers as first author, senior author, or contributing middle author, reflecting substantive contributions to computational modeling, machine learning, AI-driven research, or digital health studies.

Requirements

Master’s Degree in Computer Science, Information Sciences, Statistics and Decision Science, Social Psychology, or similar.
Good Clinical Practice (GCP)
Experience with Machine Learning Generative AI, Natural Language Processing / LLMs, and Computational Simulation Modeling.
Experience with Multimodal Time-Series Data Analysis, Reproducible Data Science (Python/R) - Human Subjects, Research Support, and Manuscript writing.
Experience with Ordering Supplies, Data Presentation, Data Analysis, and SOP writing.
Skilled in SQL, Pytorch, TensorFlow, Hugging Face, Pandas, Python, MATLAB, Linux, R and Open AI.
Skilled in API Integration, AWS, Azure, GitHub, Jupyter Notebook, RedCap, Mobile Sensing Platforms.

Nice To Haves

Ability to multi-task and pay close attention to detail.
Excellent analytical, organizational and time management skills.
Strong communication skills, both oral and written.

Responsibilities

Design and conduct experiments and studies requiring the application of a broad professional knowledge of scientific theories and principles.
Advance the science through proposing and investigating new target projects for initiation based on novelty and relevance to human health.
Develop and implement computational, statistical, and simulation-based experimental designs to evaluate behavioral and digital health research hypotheses 1
Work with staff on the interpretation of experiments performed by research scientists.
Independently direct and conduct special research projects that have a high priority for the laboratory with special emphasis in the development of novel research treatments.
Work with postdoc/biological scientists to perform statistical analysis for research projects.
Review scientific data to determine if a research program can be developed around an idea or data package.
Provide technical guidance to laboratory staff on computational modeling, machine learning methods, and data analysis strategies.
Operate, troubleshoot and train others to use laboratory equipment and software.
Provide advice and assistance to users in designing experiments, using instrumentation, data acquisition and analysis, and preparing figures for publication.
Participate in the laboratory’s review of future proposals for resource allocations.
Write technical reports and prepare manuscripts.
Oversee project data quality; analyze and interpret project data.
Conduct bioinformatics data analyses and statistical analyses; prepare data for abstracts and manuscripts.
Maintain a detailed and up-to-date electronic laboratory notebook detailing all experiments.
Collaborate with investigators to translate research questions into quantitative modeling and data-driven study designs.
Develop and share analytical tools, scripts, and reproducible workflows to support laboratory research activities.
Meet with lab members to present updates
Documented Data Pipelines: Fully documented and reproducible data ingestion, preprocessing, quality control, and feature extraction pipelines for all assigned multimodal datasets.
Curated Analytical Datasets: Cleaned, integrated, and analysis-ready datasets with accompanying data dictionaries and processing documentation.
Validated Models: Implemented and evaluated machine learning, NLP, and/or generative AI models with documented methodology, performance metrics, validation results, and interpretation of findings.
Simulation Models: Developed computational simulation models (e.g., agent-based, predictive, or generative agent models) with written documentation of assumptions, parameterization, calibration, and sensitivity analyses.
Reproducible Code Repositories: Version-controlled, well-documented code repositories (with clear README files) sufficient for replication by other researchers.
Technical Documentation: Written model documentation describing design decisions, input features, target definitions, modeling tradeoffs, and limitations.
Progress Summaries: Written summaries of completed analyses, model performance, dataset updates, and next steps.
Manuscript Drafts and Figures: Draft and revised scientific manuscripts, methods and results sections, tables, and figures suitable for peer-reviewed publication.
Presentations: Slide decks and presentation materials for internal reviews, lab meetings, and scientific conferences.
Subjects Research Support Documentation: Completed consent documentation support, data collection tracking logs, and documentation of participant-facing digital data procedures.