Technical Epidemiologist
Role Overview:
This position provides high-level technical assistance in statistical analysis, study design, and predictive modeling for the Department of Health (DOH). The primary focus is on public health surveillance and reporting to mitigate urgent public health issues, specifically drug overdoses and firearm violence.
Core Responsibilities:
Plan, design, and manage injury surveillance and reporting systems for unintentional injuries, drug overdoses, and firearm-related incidents.
Develop comprehensive analytic datasets, including extraction, cleaning, record deduplication, and variable coding.
Document all data processes thoroughly in standardized codebooks and protocols.
Create complex data visualizations and dashboards using Tableau for both internal and public-facing reports.
Identify and implement statistical modeling approaches to inform community response and prevention strategies.
Utilize software tools to identify spatial or temporal clustering and perform predictive modeling.
Develop monitoring and evaluation frameworks for injury surveillance activities.
Act as a consultant in biostatistics and epidemiological methods for the analysis and interpretation of agency data.
Perform data linkages to connect overdose and violence-related data with other various program datasets.
Monitor quality assurance and quality improvement (QA/QI) processes to ensure data integrity and standardization.
Required Qualifications
Education:
Bachelor's or Master's degree in Public Health, Epidemiology, or Statistics from an accredited institution.
Experience:
Minimum of 1 year of epidemiology experience in an academic or state/local health department setting.
Technical Skills:
Proficiency in SAS or Tableau for data management, analysis, and visualization. Data Analytics: Proven experience in study design, business analytics, and data cleaning processes.
Preferred Qualifications
Education:
Terminal degree (PhD or DrPH) in Public Health or a related field.
Automation: Experience using R or Python for automating data processing and reporting.
Advanced Modeling: Experience with multi-level and hierarchical modeling.
Research: A strong history of relevant, peer-reviewed publications.
