Data Scientist, Ambulatory Transformation & Performance

General Information

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Work Location: Los Angeles, USA Onsite or Remote Flexible Hybrid Work Schedule Monday-Friday 8am-5pm Posted Date 10/18/2024 Salary Range: $102500 - 227700 Annually Employment Type 2 - Staff: Career Duration Indefinite Job # 19946

Primary Duties and Responsibilities

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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
  
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