Data Scientist

CDC Foundation

7h•$92,700 - $134,275•Remote

About The Position

The Senior Data Scientist will play a crucial role in advancing the CDC Foundation's mission by leveraging data to inform strategic decisions and initiatives in a public health organization. This role is aligned to the Workforce Acceleration Initiative (WAI). WAI is a federally funded CDC Foundation program with the goal of helping the nation’s public health agencies by providing them with the technology and data experts they need to accelerate their information system improvements. Working within Prince George’s County Health Department (PGCHD), the Data and Informatics Specialist will use advanced analytics and statistical techniques to analyze public health data and generate insights that support reporting, program evaluation, and operational decision-making. This role focuses on working with analytics-ready datasets, contributing to data modeling, and supporting the development and design of analytics solutions using tools such as SQL, Power BI, Excel, and Azure-based analytics platforms (e.g., Azure Synapse Analytics) to identify trends and patterns that inform public health programs, performance monitoring, and strategic priorities. The Senior Data Scientist will be hired by the CDC Foundation and assigned to the Prince George’s County Health Department (PGCHD). This position is eligible for a fully remote work arrangement for U.S. based candidates.

Requirements

Bachelor's degree in data science, Statistics, Computer Science, Public Health, Informatics, or related field (master's degree preferred).
Minimum 5 years of experience in data science, advanced analytics, or applied analytics roles.
Strong proficiency in Python and/or R for statistical analysis and machine learning.
Experience working with SQL and large analytical datasets in cloud or data warehouse environments.
Hands-on experience with Power BI, including data modeling, DAX, and dashboard design.
Solid understanding of statistical methods, predictive modeling, and data validation techniques.
Ability to communicate complex analytical concepts clearly to non-technical stakeholders.
Strong analytical thinking, problem-solving, and data storytelling skills.
Up to 10% domestic travel may be required.

Nice To Haves

Hands-on experience with Azure Synapse Analytics, Spark pools, and ADLS Gen2.
Familiarity with Medallion Architecture or Lakehouse-based analytics platforms.
Experience with Azure Machine Learning, cognitive services, or AI-driven analytics solutions.
Background in healthcare, public health, or life sciences analytics.
Knowledge of HIPAA, healthcare data standards, and public-sector data governance requirements.
Experience deploying or operationalizing ML models in production or semi-production environments.
Familiarity with other analytics and visualization tools (e.g., Tableau, SAS, R Shiny).

Responsibilities

Develop and deliver advanced analytics solutions using curated Silver and Gold layer datasets within Azure Synapse Analytics.
Apply statistical analysis, machine learning, and predictive modeling techniques to identify trends, risks, and outcomes in healthcare and public health data.
Design and validate models for forecasting, classification, clustering, and anomaly detection to support population health, program evaluation, and operational decision-making.
Collaborate with data engineering teams to define analytical requirements for Bronze, Silver, and Gold data layers.
Utilize Azure Synapse Spark pools and SQL pools to explore, analyze, and prepare data for advanced analytics and modeling.
Ensure analytical outputs are reproducible, scalable, and aligned with enterprise data standards.
Design and develop Power BI dashboards, reports, and semantic models that communicate insights to technical and non-technical stakeholders.
Translate complex analytical findings into clear, actionable narratives for public health leaders and program teams.
Support ad hoc analysis and self-service analytics use cases across the organization.
Apply AI and ML techniques using Azure-native or open-source tools (e.g., Azure Machine Learning, Python ML libraries).
Explore cognitive analytics use cases such as natural language processing, risk stratification, pattern recognition, and decision-support augmentation.
Collaborate with stakeholders to evaluate feasibility, ethics, and impact of AI-driven solutions in healthcare and public health contexts.
Partner with data engineering and governance teams to ensure data quality, lineage, and trustworthiness of analytical datasets.
Follow data privacy, security, and ethical AI principles, particularly for protected health information (PHI).
Support documentation and transparency of analytical methods, assumptions, and limitations.
Work closely with epidemiologists, informaticians, analysts, and IT teams to align analytics initiatives with public health priorities.
Present findings, recommendations, and model results to diverse stakeholder groups.
Mentor analysts and junior data scientists on analytical methods, tools, and best practices.