• Break down multi-domain business problems with a strong focus on return on investment and impact metrics, and think through the appropriate technical methods from POC to production • Develop solutions and extract insight from data using data science and classical machine learning methods and generative AI (GenAI) • Take ownership of developing, implementing, and automating robust data science and ML models and pipelines, including feature generation, modeling, evaluation, retraining and deployment • Translate sophisticated analytical findings and model outcomes into clear, actionable insights for stakeholders, demonstrably driving value and improvements in key business metrics • Collaborate with cross-functional teams to understand requirements and deliver innovative Data Science/ML/AI solutions • Conduct research on the latest Data Science/ML/AI developments and apply them to improve our systems • Document processes, code, and findings for future reference and knowledge sharing

• Explore data sources and develop new PropertyTech and InsurTech models using data science, including machine learning and deep learning. • Research, design, and implement deep learning algorithms and architectures that extract insights from imagery sources such as aerial imagery and geospatial data. • Lead the development of high-quality datasets for robust model training and evaluation. • Collaborate closely with cross-functional teams to understand requirements and translate product, engineering, and business constraints and questions into actionable data science problems. • Coach and provide feedback to members of the Data Science and Machine Learning team, fostering skill development and knowledge sharing.

• Design, develop, and deploy machine learning and data science solutions using Python. • Build and maintain reusable Python libraries and analytical frameworks. • Develop machine learning models, data visualization solutions, and analytical dashboards. • Build and deploy APIs and cloud-based applications. • Implement and maintain CI/CD pipelines for machine learning workflows. • Deploy, monitor, and optimize machine learning models on Microsoft Azure. • Collaborate with business stakeholders to translate business requirements into technical solutions. • Maintain technical documentation and follow software engineering best practices. • Stay up to date with emerging technologies and industry trends relevant to the role.

• Serve as a technical lead on AI and machine learning initiatives, providing guidance on solution architecture, model selection, implementation approaches, and technical best practices. • Mentor and support junior and mid-level data scientists through code reviews, knowledge sharing, technical coaching, and collaborative problem solving. • Establish and promote best practices for MLOps, model evaluation, model monitoring, reproducibility, and responsible AI development • Build and deploy end-to-end ML pipelines on AWS (e.g., SageMaker, S3, Glue) for scalable training, evaluation, and inference. • Develop and implement advanced NLP solutions, including text classification, entity recognition, topic modeling, and semantic search using models such as BERT and transformer-based architectures. • Design, build, and productionize RAG (Retrieval-Augmented Generation) systems, including document ingestion, embedding pipelines, vector search, and LLM orchestration. • Develop LLM-powered applications, including prompt engineering, evaluation frameworks, and optimization techniques for accuracy, consistency, and cost. • Contribute to agentic AI system design, including multi-step reasoning workflows, tool use, and orchestration of LLM-driven agents for complex tasks. • Implement predictive analytics and statistical modeling to uncover patterns, trends, and insights from healthcare data. • Evaluate emerging AI technologies, frameworks, and techniques and recommend their appropriate application to government healthcare use cases. • Perform data mining and exploratory data analysis (EDA) using state-of-the-art techniques across structured and unstructured datasets. • Contribute to technical leadership across multiple AI initiatives while remaining an active hands-on developer and model builder. • Build data visualizations, dashboards, and analytical tools to communicate findings clearly to technical and non-technical stakeholders. • Evaluate model performance using appropriate metrics (e.g., accuracy, AUC, precision/recall) and present results in a clear, actionable manner. • Collaborate in an Agile environment with cross-functional teams including engineers, analysts, and stakeholders. • Recommend data-driven solutions and AI strategies aligned with CMS business needs and healthcare policy objectives.