Data Science & Analytics Lead

ASRT, Inc.•Atlanta, GA

19h•Onsite

About The Position

ASRT is seeking a Data Science & Analytics Lead to provide senior technical leadership for The Center for Disease Control and Prevention (CDC) healthcare-data modernization, advanced analytics, clinical-data interoperability, enterprise data architecture, and data-science strategy. This individual will advise CDC leadership and scientific teams on how to integrate large healthcare datasets, interoperability standards, common data models, cloud technologies, metadata, governance, and advanced analytics into scalable and secure public-health surveillance solutions. This position represents one of the most specialized roles in Task Area 2 and requires a combination of healthcare informatics, data science, interoperability, analytics, and enterprise technology experience.

Requirements

Master’s degree in Health Informatics, Data Science, Epidemiology, Statistics, Economics, or related discipline, consistent with CDC’s Task 14 requirement.
8 years of progressively responsible data science, Health Informatics, analytics, healthcare-data, or information-systems experience.
Significant experience using R, Python, and SQL.
Experience managing and analyzing large healthcare datasets.
Demonstrated knowledge of healthcare interoperability standards, including HL7 and FHIR.
Experience with healthcare common data models.
Experience with Power BI or comparable visualization platforms.
Experience with cloud-based analytics.
Experience translating technical/data-science requirements into recommendations for scientific or executive stakeholders.
Candidates should possess at least five years of relevant data analysis experience, consistent with the approved GSA labor category, with senior level candidates expected to demonstrate substantially greater depth.
Experience should include complex data analysis, data quality management, reporting, dashboards or visualization, development of data definitions and business rules, and communication of analytical results to stakeholders.
Highly relevant experience includes Federal or state public health analytics, cardiovascular or chronic-disease data, GIS and small area analysis, market research or population segmentation data, statistical modeling, epidemiologic or surveillance data, scientific writing, and support to CDC or HHS programs.
Health informatics and healthcare-data architecture.
Healthcare-data interoperability.
Common data models.
Clinical-data analytics.
EHR and claims data.
Large healthcare/public-health datasets.
Metadata, data lineage, provenance, stewardship, and governance.
Enterprise data architecture.
Cloud-based and distributed analytics.
Machine-learning and advanced-analytics methods.
Electronic phenotypes and clinical case definitions.
Technical roadmapping and solution assessment.
HL7 – demonstrated knowledge/experience required.
FHIR – demonstrated knowledge/experience required.
OMOP or comparable healthcare common data model experience.
Python – advanced proficiency.
R – advanced proficiency.
SQL – advanced proficiency.
Microsoft Power BI or comparable enterprise visualization platform.
Familiarity with distributed-computing technologies and large relational databases.
Familiarity with metadata/catalog/governance tools.
Excellent oral and written communication skills in English.
Must be a United States citizen or permanent resident or have authorization for employment in the United States.

Nice To Haves

PhD or doctoral work in Health Informatics, Data Science, Epidemiology, Statistics, or Economics.
OMOP/OHDSI expertise.
HealthVerity, Premier, Truveta, IQVIA, PCORnet, MarketScan, or comparable commercial/aggregated healthcare datasets.
CMS, HRSA, VA, DHA, or other Federal healthcare datasets.
Electronic phenotypes and clinical case definitions.
Distributed computing and large relational databases.
Microsoft Azure.
CDC EDAV/1CDP.
CDC Data Modernization Initiative experience.
Clinical quality measures and public-health surveillance using EHR/claims data.
Federal healthcare data modernization.
The preferred candidate will demonstrate advanced capability with SAS, SUDAAN, R, Python, SQL, ArcGIS, Tableau, Power BI, and comparable analytical and visualization technologies.
Experience with CDC enterprise environments, including EDAV, CDC PLACES, BRFSS, CDC WONDER, REDCap, SharePoint, and Federal data-governance processes, is highly desirable.
Experience developing reproducible analytical workflows, data dictionaries, codebooks, annotated programs, quality control procedures, dashboards, accessible electronic products, scientific reports, and health communication materials is strongly preferred.
Previous experience working in a similar role
Azure or comparable Federal/cloud analytics platform highly desirable.
Familiarity with EDAV and 1CDP highly desirable.
Familiarity with Git/Azure DevOps, CI/CD, or comparable development-governance environments desirable.
Knowledge of NIST/FISMA/FedRAMP requirements in Federal data environments desirable.

Responsibilities

Lead assessment of CDC data, analytical, and information-system requirements.
Recommend modern analytical and computational architectures.
Provide senior guidance on data science, analytics, machine learning, visualization, cloud, and CDC-approved AI approaches.
Support integration and modernization of data systems and enterprise services.
Advise on scientific-data curation, metadata, provenance, lineage, governance, and stewardship.
Design strategies for management and analysis of large healthcare datasets.
Advise on cloud-based and distributed analytical environments.
Support development and implementation of enterprise data and metadata standards.
Provide technical leadership concerning clinical-data interoperability and common data models.
Guide development, validation, and management of electronic phenotypes and clinical case definitions.
Support the use of EHR, claims, clinical, pharmaceutical, and commercial healthcare datasets for public-health surveillance.
Collaborate with CDC technical governance, leadership, epidemiologists, statisticians, and scientific stakeholders.
Develop roadmaps for enterprise data, analytics, and knowledge-management systems.
Evaluate technical problems and recommend corrective actions.
Ensure technical solutions align with CDC enterprise platforms, governance, architecture, and security requirements.
Support migration from siloed analytical environments into enterprise capabilities where appropriate.