egen - Principal Data Architect (Data Science)

• Data Ingestion: Design and build the pipelines that bring data in for feature engineering and machine learning. • Feature Engineering: Use your knowledge of healthcare data, statistics, and data science to create features with real predictive power. • Feature Extraction and Selection: Use techniques like clustering, text analytics, and PCA to find the most useful signals while preserving the meaning in the original data, so models stay accurate and scale well. • Exploratory Data Analysis: Explore the data to find patterns, trends, and relationships, and use what you learn to match features with the right modeling approach. • Model Development: Experiment with statistical and machine learning methods to build, tune, and optimize predictive models that fit the use case. • Deployment: Ship production code that follows engineering best practices. • Documentation and Communication: Document your work and share your approach, insights, and solutions with technical and non-technical audiences. • Monitoring: Build and maintain the pipelines and processes that keep our enterprise data science products running smoothly. • Quality and Integrity: Keep data and outputs accurate and reliable from start to finish. • Collaboration: Partner with stakeholders, data scientists, data engineers, and product managers to build and deliver great data products.