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The Data Manager oversees the quality and timeliness of all data management activities for ongoing NIH-funded longitudinally followed-up pregnancy and child cohorts as well as an NIH-funded consortium under ECHO (Environmental Influences on Child Health Outcomes) Program. The Data Manager is responsible for overseeing and supervising the data management team consisting of a number of data coordinators, in regards to comprehensive data cleaning and dataset generation, query tracking, development of effective scripts for data cleaning and error proofing, compilation and maintenance of data cleaning protocols and data management documentations, data harmonization across study sites, as well as creating reports and overseeing data transfer process within the ECHO Program. The Data Manager develops and monitors all data related metrics, ensures appropriate training for the data management team, and reports to the Principal Investigator and Project Director periodically.
- Supervises the entire data management team for comprehensive data cleaning and harmonization across all study sites and cohorts. Responsible for all essential and complex data management for multiple ongoing projects. Reviews and validates all the statistical logics invovled in data cleaning and management process to ensure consistency, integrity and accuracy based on specific guidelines
- Creates, updates, maintains and validates all data for the generation and provision of the datasets to the investigators team for further complex analyses as well as computerized reports. Manages data through review, computerization, cleaning and auditing for complaince with standard operating procedures, and regulatory agency guidelines. Responsible for all comprehensive error-proof cleaning on large and complex datasets with multiple and ongoing study visits and its documentation, and designing effective validation scripts/coding using SAS and R to perform complete data validation and error detection
- Develops, updates, and maintains cohort-specific data dictionaries, data quality assurance documentation and data management guidelines. Oversees the data integrity, contents and its management, ensuring that all investigators and the data collection team are informed of any issues relating to data integrity. Documents datasets using specifications worked out in conjunction with data collection team and investigators group
- Provides guidance and appropriate training to all data coordinators in the data management team, as well as investigators in the research team when needed. Available to guide and help junior data coordinators, all investigators and junior researchers, and other staff with the use of datasets and in general introduction to the system and the statistical software
- Oversees and coordinates activities required to meet quality standards and timelines of projects. Ensures that the data management team is proceeding according to plans and assigns tasks to the data coordinators accordingly. Develops recording systems to ensure that the flow is tracked in an accurate and timely fashion
- Oversees the communications between the data management team and the data collection team. Provides insights and assists on development of training plans for data collection team to ensure data collection quality. Recognizes recurring data collection issues and identifies causes in order to reach a solution. Generate plans and provide tools/techniques for improvement and to measure success
- Ensures data accuracy through review of case report forms or questionnaires for completeness and consistency with the REDCap database, and communicates with the REDCap specialist to ensure/improve the database structure that fulfills the need and feasibility for both the data management team and the data collection team
- Creates reports and summary statistics that are required for NIH progress reports and future grant submissions by the Pricinpal Investigator and the research/investigators team
- Assists in standardizing data management procedures for different purposes or circumstances such as documentation for departmental operating procedures or reports
- Maintains knowledge of current regulations and technologies related to data management
- Performs other duties as assigned by the Principal Investigator and the Project Director
- Education: Master’s degree in Biostatistics preferred
- Experience: Minimum of 5 years of experience in programming and data management related to medical/clinical research including experience in working on large longitudinal epidemiological studies and/or large databases
- Minimum of 5 years of SAS experience as well as epidemiologic data management experience preferred. Fluency in R and experience in REDCap and ArcGIS are also preferred
- Certificate or Diploma in Quality Assurance preferred
- Excellent communication and writing skills, organizational and planning skills
The Mount Sinai Health System believes that diversity is a driver for excellence. We share a common devotion to delivering exceptional patient care. Yet we’re as diverse as the city we call home- culturally, ethically, in outlook and lifestyle. When you join us, you become a part of Mount Sinai’s unrivaled record of achievement, education and advancement as we revolutionize medicine together.
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Over 38,000 employees strong, the mission of the Mount Sinai Health System is to provide compassionate patient care with seamless coordination and to advance medicine through unrivaled education, research, and outreach in the many diverse communities we serve.
Formed in September 2013, The Mount Sinai Health System combines the excellence of the Icahn School of Medicine at Mount Sinai with seven premier hospital campuses, including Mount Sinai Beth Israel, Mount Sinai Beth Israel Brooklyn, The Mount Sinai Hospital, Mount Sinai Queens, Mount Sinai West (formerly Mount Sinai Roosevelt), Mount Sinai St. Luke’s, and New York Eye and Ear Infirmary of Mount Sinai.
The Mount Sinai Health System is an equal opportunity employer. We comply with applicable Federal civil rights laws and does not discriminate, exclude, or treat people differently on the basis of race, color, national origin, age, religion, disability, sex, sexual orientation, gender identity, or gender expression.