Data Scientist - Quality Department - Flexible Location
Role summary
The Data Scientist will leverage advanced analytics, modeling, and predictive insights to support strategic planning, program evaluation, risk identification, and long-term organizational decision-making. This role moves beyond routine reporting to address deeper analytical questions, scenario modeling, and strategic insights. Key responsibilities include developing predictive and statistical models, performing trend analysis, supporting forecasting and capacity planning, and identifying risk indicators. The Data Scientist will collaborate with data engineering and IT to design scalable solutions, define data requirements, and integrate new data sources. They will also translate complex analytical results into clear, actionable recommendations for both technical and non-technical audiences, creating visualizations and documenting methodologies.
The pay for this position is between $100,000 and $115,000 annually.
Data Scientist
Position Overview
The Data Scientist is responsible for advanced analytics, modeling, and predictive insights that support strategic planning, program evaluation, risk identification, and long-term organizational decision making. This role builds on the foundation of reliable reporting and well defined metrics to move the organization from descriptive reporting to forward looking analysis.
This position focuses on identifying trends, patterns, and relationships in data that are not immediately visible through standard reporting and translating those findings into actionable insight for leadership.
The Data Scientist role is not focused on routine reporting. It is focused on deeper analytical questions, scenario modeling, and strategic insight.
Core Responsibilities
Advanced Analytics and Modeling
- Develop predictive and statistical models related to utilization, outcomes, staffing, access, and financial performance
- Perform trend analysis and pattern detection across clinical, operational, and financial datasets
- Support forecasting, capacity planning, and scenario analysis for leadership
- Identify risk indicators and early warning signals relevant to programs and operations
Strategic and Program Evaluation
- Partner with Quality leadership to evaluate program effectiveness and outcome trends
- Support Finance with long term financial modeling and impact analysis
- Assist leadership with data driven evaluation of new initiatives and service changes
- Support funding stream analysis, performance monitoring, and outcome measurement
- Collaborates with Quality and Clinical leadership to identify and synthesize data and data trends into actionable operational frameworks
- Works in collaboration with quality and training team members as a translator between non technical stakeholders and technical teams
Technical Collaboration and Architecture
- Work closely with data engineering and IT to design scalable analytical solutions
- Define data requirements for advanced analytical use cases
- Evaluate and integrate new data sources as needed
- Ensure analytical models are reproducible, documented, and interpretable
Communication and Insight Delivery
- Translate complex analytical results into clear, actionable recommendations
- Present findings to technical and non-technical audiences
- Create visualizations and summaries that support executive decision making
- Document methodologies, assumptions, and limitations
Required Qualifications
- Master’s degree in data science, statistics, applied mathematics, analytics, or related field or commensurate experience
- Strong experience with statistical analysis and modeling techniques
- Proficiency in Python, R, or similar analytical languages
- Experience working with large, complex datasets
- Strong understanding of data structures and analytical workflows
- Ability to clearly communicate complex concepts to diverse audiences
Preferred Qualifications
- Experience in healthcare, behavioral health, or human services analytics
- Experience with predictive modeling in regulated or mission driven environments
- Familiarity with EMR or EHR data
- Experience supporting executive level decision making