Principal Data Scientist

Job Description
Job Summary
Responsible for overseeing data science projects, managing and mentoring a team, and aligning data initiatives with business goals. Lead the development and implementation of data models, collaborate with cross-functional teams, and stay updated on industry trends. Ensure ethical data use and communicate complex technical concepts to non-technical stakeholders. Lead initiatives on model governance and model ops to align with regulatory and security requirements. This role requires technical expertise, strategic thinking, and leadership to drive data-driven decision-making within the organization and be the pioneer on generative AI healthcare solutions, aimed at revolutionizing healthcare operations as well as enhancing member experience.

Job Duties

• Research and Development: Stay current with the latest advancements in AI and machine learning and apply these insights to improve existing models and develop new methodologies.
• AI Model Deployment, Monitoring & Model Governance: Deploy AI models into production environments, monitor their performance, and adjust as necessary to maintain accuracy and effectiveness and meet all governance and regulatory requirements.
• Innovation Projects: Lead pilot projects to test and implement new AI technologies within the organization
• Data Analysis and Interpretation: Extract meaningful insights from complex datasets, identify patterns, and interpret data to inform strategic decision-making.
• Machine Learning Model Development: Design, develop, and train machine learning models using a variety of algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Agentic Workflows Implementation: Develop and implement agentic workflows that utilize AI agents for autonomous task execution, enhancing operational efficiency and decision-making capabilities.
• RAG Pattern Utilization: Employ retrieval-augmented generation patterns to improve the performance of language models, ensuring they can access and utilize external knowledge effectively to enhance their outputs.
• Model Fine-Tuning: Fine-tune pre-trained models to adapt them to specific tasks or datasets, ensuring optimal performance and relevance in various applications.
• Data Cleaning and Preprocessing: Prepare data for analysis by performing data cleaning, handling missing values, and removing outliers to ensure high-quality inputs for modeling.
• Collaboration: Work closely with cross-functional teams, including software engineers, product managers, and business analysts, to integrate AI solutions into existing systems and processes.
• Documentation and Reporting: Create comprehensive documentation of models, methodologies, and results; communicate findings clearly to non-technical stakeholders.
• Mentors, coaches, and provides guidance to newer data scientists.
• Partner closely with business and other technology teams to build ML models which helps in improving Star ratings, reduce care gap and other business objectives.
• Present complex analytical information to all level of audiences in a clear and concise manner Collaborate with analytics team, assigning and managing delivery of analytical projects as appropriate
• Perform other duties as business requirements change, looking out for data solutions and technology enabled solution opportunities and make referrals to the appropriate team members in building out