Quality Data Analyst

Responsibilities:

• Design analyses and deliver information to drive clinical, operational, and/or strategic decisions to improve recovery-oriented, person-centered care.
• Develop research methods and data analysis plans related to health outcomes, quality of care, health equity, service utilization, trends, and forecasting.
• Perform analyses explaining trends, changes, and outliers to drive systemic change.
• Coordinate analytics / information development through the agency and external organizations, as needed.
• Assist with, and prepare presentations to staff, leadership, partner organizations, community groups, regulatory agencies, and others, as needed.
• Create reports, dashboards, and analyses to be shared with departmental and organizational leaders, staff, partner organizations, community groups, regulatory agencies, and others, as needed.
• Ensure data accuracy and integrity and use data-storytelling to inform the needs of the audience.
• Manages multi-disciplinary relationships to assure collaboration and effective/efficient operations.
• Act as a technical resource for staff for best practices in the process of data collection, and creation of meaningful information.
• Ensure reporting consistently includes a focus on identifying and communicating health and/or access disparities within our community.
• Collaborate with multi-disciplinary staff to provide data collection and analysis for identifying opportunities for improvement; provide recommendations based on analyses.
• Gather and document business requirements, operational methodologies, and workflows to perform and/or support existing system analysis and new systems recommendations.
• Coordinate with internal and external stakeholders to identify and support information development to address organizational needs.
• Develop and maintain deep understanding of data, data infrastructure, and processes used for developing clinical, operational and/or program performance information.

Key Skills:

• Structured Query Language.
• Microsoft Excel.
• Critical thinking.
• R or Python statistical programming.
• Data visualization.
• Presentation skills.
• Machine learning.