The Data Analyst leads and supports the American Council of Academic Physical Therapy (ACAPT)’s data strategy, analytics, and business intelligence efforts. ACAPT is a national nonprofit organization representing academic physical therapy programs and advancing excellence in education, leadership, and impact across the profession.

This role is responsible for managing the full data lifecycle, from data ingestion, governance, and infrastructure to analysis, modeling, and visualization, ensuring data is accurate, accessible, and actionable across the organization.

In a small, fully remote team environment, the Data Analyst plays a critical role in translating complex data into insights that inform strategy, operations, advocacy, and member engagement. The ideal candidate is both technically strong and highly collaborative, with the ability to work independently while partnering closely with leadership and the Marketing and Communications team.

ACAPT values work-life balance, sustainable workloads, and focused impact rather than nonstop escalation, and is committed to supporting staff success without the expectations often found in high-pressure tech environments. This role offers meaningful responsibility, visibility, and impact within a mission-driven organization that prioritizes thoughtful, ethical, and human-centered use of data.

Key Responsibilities

Data Strategy and Management

• Support the organization’s broad spectrum of data management requirements, including data strategy, data modeling, and documentation.
• Lead the conceptualization, development, and ongoing evolution of ACAPT’s Center for Excellence, aligning data assets, analytics, and reporting with organizational strategy and member value.
• Identify and manage data feeds across internal systems and external sources.
• Oversee data infrastructure needs, including data ingestion, automation, and transformation processes.
• Maintain metadata management practices and support data governance to ensure consistency, integrity, and responsible data use across the organization.

Analytics and Business Intelligence

• Design and execute statistical analyses on large, complex datasets to support organizational decision-making.
• Oversee the intake, management, and analysis of organizational survey data, including recurring and ad hoc data collection efforts.
• Serve as the primary staff lead for the Institutional Profile Survey (IPS), overseeing survey design support, data collection, quality assurance, analysis, and reporting.
• Develop, maintain, and optimize dashboards and reports using Qualtrics, Tableau, and related BI tools.
• Stay current on Tableau features, visualization best practices, and emerging business intelligence trends.

Machine Learning and Advanced Analytics

• Build and develop machine learning models across all phases of development, including design, training, evaluation, and implementation.
• Support applied use cases for machine learning and generative AI, including testing and evaluation of solutions aligned with organizational goals.

Visualization and Communication

• Apply machine learning, predictive analytics, and data forecasting techniques to support organizational insights and strategic decision-making.
• Manage and optimize existing analytical models, automated processes, and data workflows that support routine reporting and operational tasks.
• Support the evaluation, implementation, and responsible use of machine learning and generative AI tools aligned with organizational needs and capacity.
• Monitor emerging technologies and analytical approaches to enhance forecasting, automation, and data-driven insights over time.

Organizational Enablement

• Serve as the staff liaison to the Data Advisory Committee, supporting agenda development, data-informed discussions, and alignment with organizational priorities.
• Promote data literacy and standardized data practices across the organization to improve data-informed decision-making.

• Bachelor’s degree in quantitative fields such as Mathematics, Statistics, Economics, or Computer Science or equivalent professional experience demonstrating the ability to design, analyze, and communicate insights from complex datasets.
• Experience with software development practices, development lifecycles, and associated tools.
• Demonstrated ability to retrieve, organize, and analyze data from a wide range of sources and data structures.
• Experience building and developing machine learning models across the full lifecycle.
• Ability to work effectively in a small team environment to solve complex problems.
• Commitment to promoting data literacy and standardized practices across an organization.
• Candidates must be authorized to work in the United States.