Data Scientist

 Overview:

This Data Scientist is a member of an interdisciplinary team of public sector consultants assisting Florida in reaching its strategic public health goals. The Data Scientist will support epidemiological research in Florida by conducting advanced statistical modeling and population estimation. This role will contribute to public health strategies by leveraging data linkage, statistical methodologies, and geospatial analysis to provide actionable insights for policymakers and stakeholders.

The ideal candidate will have experience working with large-scale datasets, statistical modeling, and data visualization, with previous experience in epidemiology, preferably related to opioid abatement. This is an individual contributor role, where the selected candidate will act as a contributing member of a larger project team, under the direction and supervision of the designated project manager.

This is a fully remote role with the expectation of some required travel to Florida throughout the engagement. 

Essential Functions:

• Utilize Capture-Recapture methods to cross-reference multiple data sources and estimate population sizes.
• Implement Multi-Source Estimation techniques, controlling for duplicates and applying predictive modeling for accurate population estimation.
• Apply Epidemiological Multiplier methodologies to adjust for underreporting and missing data.
• Conduct Triangulation Analysis by overlaying datasets to identify trends, correlations, and disparities.
• Use Bootstrap Methods and construct Confidence Intervals to assess model precision.
• Perform Sensitivity Analysis to evaluate the impact of data variability on estimates.
• Develop Geospatial Mapping & Data Visualization using tools such as ArcGIS or QGIS.
• Conduct Model Calibration & Validation to ensure statistical reliability and alignment with real-world data.

• Clean and preprocess large-scale epidemiological and demographic datasets.
• Advise ISF staff on data linkage techniques to match individuals across datasets accurately.
• Ensure compliance with data governance, confidentiality regulations, and industry recognized ethical standards.

• Develop data visualizations, dashboards, and reports to communicate key findings effectively.
• Support the creation of technical reports and presentations summarizing methodologies and results.
• Communicate statistical findings to stakeholders, including government agencies and public health professionals.

• Work closely with epidemiologists, statisticians, and public health professionals to refine methodologies and validate findings.
• Stay updated on emerging trends in population estimation and epidemiological research.
• Provide recommendations for policy implications, program needs, and intervention strategies based on data analysis.

Qualifications:

• Master’s degree in Public Health, Epidemiology, Statistics, Data Science, or a related field.
• Five (5) years of experience in epidemiology, statistical modeling, or data analysis.
• Proficiency in R and Microsoft Office Suite (Excel, Word, PowerPoint).
• Experience working with large datasets and applying advanced statistical methodologies.
• Strong written and verbal communication skills for technical and non-technical audiences.

• Doctoral degree in Epidemiology, Statistics, or a related field.
• Experience with Stata, SAS, MSSQL, Power BI, ArcGIS/QGIS, and Azure Data Factory.
• Previous experience in opioid surveillance, population estimation, and/or public health research.
• Knowledge of federal and state regulations related to public health data governance.

WiredPeople provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, WiredPeople complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.