Data Scientist, Ambulatory Transformation & Performance

Description The Data Scientist plays a pivotal role in transforming raw data into actionable insights that drive the efficient operation of ambulatory clinics. This position involves advanced data modeling, statistical analysis, and the application of machine learning techniques to identify trends, optimize performance, and support data-driven decision-making. The Data Scientist will design and maintain robust data models, develop predictive models, and collaborate with stakeholders to interpret complex data sets. The role requires expertise in data analysis, a strong understanding of healthcare data, and a commitment to enhancing the financial viability and operational efficiency of ambulatory clinics. Develop, refine, and maintain complex data models that support ambulatory operations, ensuring data accuracy and consistency. Apply statistical analysis and machine learning techniques to analyze large datasets, identify trends, and generate predictive insights. Design and implement predictive models to forecast key performance indicators, patient outcomes, and ambulatory operational efficiencies. Create and validate algorithms for data mining, cleansing, and transformation to enhance data usability Lead or participate in projects focused on enhancing data infrastructure, analytical capabilities, and reporting frameworks. Explore and implement innovative data science techniques and tools to address complex challenges in ambulatory operations. Stay current with advancements in data science and healthcare analytics, applying new methods and technologies to improve performance and outcomes. Work closely with cross-functional teams, including IT, clinical, and business stakeholders, to understand data needs and deliver actionable insights. Present findings and recommendations to leadership and other stakeholders in a clear, concise, and actionable manner. Provide guidance and mentorship to analysts within the team Lead efforts in data integration, ensuring seamless interoperability between multiple data sources and systems Enforce data governance standards, including data quality, metadata management, and data security protocols. Develop and manage ETL (Extract, Transform, Load) processes to curate data from various sources into structured formats suitable for analysis. Document and maintain data lineage, ownership, and access requirements, ensuring compliance with healthcare regulations salary range: $102500-$227700 Qualifications Required Skills and Experience: Bachelor's degree in a related field or equivalent experience/training. Minimum of 3 years of experience in a healthcare-related organization, with a strong understanding of healthcare data and operations Minimum of 5 years of experience with Python or R for data analysis, modeling, and machine learning applications. Proven expertise in data modeling, information design, and data integration. Advanced knowledge of data management systems, practices, and standards. Experience with complex data quality, governance issues, and data conversion. Strong analytical and problem-solving skills with attention to detail. Ability to abstract and represent information flows in systems through effective modeling. Excellent communication and interpersonal skills, with a demonstrated ability to work collaboratively across diverse teams. Preferred Skills: Experience with Databricks, including managing and processing large datasets in a distributed environment. Experience with Azure DevOps, including managing workflows, version control, and collaborative project management.