
Senior Database Analyst
NYU Grossman School of Medicine
NEW YORK, NYJobSeen 5 days agoSeen in employer's feed 5 days ago
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Job overview
The Senior Database Analyst will work in the Digital Health and Computational Psychiatry Program in the Department of Psychiatry, designing, developing, and maintaining relational and non-relational databases for multimodal clinical research. The role supports data harmonization, research governance, analytics, and predictive modeling, and collaborates with biostatisticians, machine learning experts, clinical investigators, and IT teams.
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Full job description
NYU Grossman School of Medicine is one of the nation's top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care. At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge. For more information, go tomed.nyu.edu, and interact with us on LinkedIn, Glassdoor, Indeed, Facebook, XandInstagram.Position Summary:We have an exciting opportunity to join our team as a Senior Database Analyst. The Senior Database Analyst will work in the interdisciplinary and cutting-edge Digital Health and Computational Psychiatry Program in the Department of Psychiatry. He/She will design, develop, and maintain relational and non-relational database systems to support complex, multimodal clinical research studies. This position requires deep expertise in data engineering, database architecture, clinical data standards, and research data governance. The ideal candidate will also leverage advanced data analytics, machine learning, and AI-driven techniques to support predictive modeling and translational research insights. The candidate will play a critical role in harmonizing diverse datasets across studies, supporting advanced statistical modeling and predictive analytics in psychiatric and translational research.Job Responsibilities: * Develop data cleaning, quality control and Extract Transform Load (ETL) routines * Maintain accurate data dictionaries and code books * Create automated dashboards, query tools, and reports to facilitate real-time data monitoring and interim analyses. * Coordinate with NYU MCIT EDW team to make updates and correct data issues in data warehouse * Create and execute plans for new data collection projects * Ensure smooth integration of data from disparate sources * Maintain familiarity with new technologies for data management * Develop tools for temperature measurement in rodent and human systems * Conduct testing in development database systems * Troubleshoot IT problems at NYULMC, interfacing with MCIT EPIC and EDW teams as well as with external IT support personnel as needed. * Ensure compliance with institutional, federal (e.g., HIPAA, 21 CFR Part 11), and sponsor-specific data governance requirements. * Develop and implement departmental SOPs for data management * Assist in managing the creation of user accounts and access permissions to folders and files, * Provide support on multiple projects to meet timelines * Perform system backups and recovery * Produce data files for further analysis by statisticians * Conduct queries and reports for basic statistical analysis for reporting purposes * Generate and maintain codebooks and data dictionaries development of big database systems * Ensure smooth integration of data acquired from disparate sources * Design, maintain and update databases and data collection tools * Collaborate with biostatisticians and machine learning experts to prepare data for advanced statistical modeling and predictive analytics * Apply machine learning algorithms (e.g., clustering, classification, regression) to clinical datasets to uncover patterns and predict patient outcomes. * Develop, validate, and maintain ML/AI models in collaboration with biostatisticians and clinical investigators. * Integrate AI tools and platforms into data pipelines to automate quality checks, anomaly detection, and outcome forecasting. * Utilize tools such as Python, R, and SQL to generate insights from structured
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