Top Remote Data & Analytics Jobs in Dayton, OH

The role involves quality assurance in asset management, focusing on trade systems, testing, defect management, data operations, and client interactions to support successful implementations.

The Technical Integration Specialist will manage data integration workflows, ensure project implementations, troubleshoot data issues, and train clients on SmithRx's integration solutions.

The Senior Business Intelligence Engineer analyzes data from various sources to resolve business problems, impacting decision-making with reports and visualizations, especially in healthcare.

The Senior Manager will lead a team in advanced analytics, providing insights to support business decisions and drive marketplace growth.

Lead the architecture and implementation of Informatica Data Marketplace, ensuring data governance and quality while facilitating client engagement and project delivery in healthcare.

The Junior Data Engineer will analyze data, build and maintain data pipelines, support CI/CD processes, and assist in IT modernization efforts for a Federal client.

The Business Analyst will gather and maintain business and system requirements, analyze financial data, translate requirements into technical specifications, and conduct workshops and briefings for Federal clients.

Lead the development and execution of pricing strategies for Quill, focusing on revenue growth and market competitiveness. Collaborate across teams, leverage analytics for pricing optimization, and establish governance frameworks to ensure pricing integrity.

As a Sr Supplier Quality Engineer Data Analyst, you'll analyze supplier quality data, develop dashboards, and collaborate with teams to enhance product quality and delivery.

The Senior Director leads strategic data and analytics initiatives, fosters collaboration, drives governance compliance, and advocates for Healthcare Analytics Solutions within the organization.

The Lead Quantitative Analytics Associate conducts statistical analyses and builds predictive models, responding to business needs and enhancing existing models. They work with large datasets, ensuring data management and model maintenance, while communicating insights effectively within the organization.