Data Analyst II

Goldbelt, Inc.•De Leon Springs, FL

About The Position

The Data Analyst II supports the mission of the Centers for Disease Control and Prevention (CDC), National Center on Birth Defects and Developmental Disabilities (NCBDDD), by providing data management, statistical analysis, surveillance support, and reporting for multi-state birth defects and Neonatal Abstinence Syndrome (NAS) surveillance activities. The position works closely with epidemiologists, surveillance program staff, information technology specialists, and state health departments to ensure the quality, integrity, timeliness, and accuracy of public health data used to inform research, prevention strategies, and public health decision-making.

Requirements

Public Health data analytics, particularly Birth Defects and Developmental Disabilities Surveillance and neonatal abstinence syndrome surveillance
Statistical programming (SAS, R, SQL, Python)
Regression modeling and trend analysis
Data visualization and reporting (Tableau, Power BI)
CDC Surveillance and Reporting Systems
Ability to Translate Complex Data into Actionable Insights
Database Management and Data Warehousing
Data Cleaning, Validation, and Quality Assessment
Analytical and Critical Thinking
Strategic Data Interpretation
Research and Evaluation Methodology
Strong Written and Oral Communication
Collaboration Across Multidisciplinary Teams
Relationship Building with State and Federal Partners
Bachelor's degree in Public Health, Epidemiology, Biostatistics, Data Science, Statistics, Health Informatics, Computer Science, or a related field.
2+ years of relevant experience
Experience managing and analyzing large public health, healthcare, or surveillance datasets.
Proficiency in SAS, R, SQL, and data visualization tools.
Knowledge of epidemiologic methods, statistical analysis, data quality management, and public health surveillance systems.
Experience preparing technical documentation and communicating analytic findings to diverse audiences.

Nice To Haves

Master's degree pr PhD in Public Health, Epidemiology, Biostatistics, Data Science, or a related discipline.
5+ years of relevant experience
Experience supporting birth defects surveillance, maternal and child health programs, developmental disabilities research, or chronic disease surveillance.
Familiarity with CDC data standards, human subjects protections, IRB processes, and federal data governance requirements.
Experience working with multi-state surveillance systems and collaborating with state health departments and public health partners.

Responsibilities

Sort, clean, and analyze birth defects and Neonatal Abstinence Syndrome (NAS) data collected from DBDID population-based birth defects surveillance systems
Prepare and maintain Human Subjects, IRB, AoC and Other Human Subjects protection documentation
Work with Information Technology (IT) staff to establish, document, and facilitate data reporting mechanisms
Establish a database variable structure for housing multi-state data submissions to CDC
Develop and execute data quality control protocols to identify data issues.
Review submissions and provide suggestions for improvement with State birth defects surveillance systems to resolve data issues
Prepare documentation of the multi-state birth defects and NAS surveillance databases including data dictionaries, file formats, and data cleaning procedures
Identify program and process improvement opportunities to increase the timeliness and accuracy of data and provide recommendations to CDC program staff; Prepare written reports of program improvement and process recommendations and analytic findings
Perform frequent (i.e., weekly) statistical analyses and reporting of data collected from DBDID birth defects surveillance systems. Generate files, reports and tables from the data analyzed in a variety of formats (e.g., SAS, CSV, XML) for CDC program staff, as requested
Utilize advanced statistical programming (e.g., SAS and R) to collect data from multiple jurisdictions cleaning data, validating the data, analyzing the data using advanced epidemiologic methods (e.g., regression models), and summarizing results.