Principal Data Architect (Data Science)
Egen · Remote
USD 180,000 to 240,000 · period not stated
RESPONSIBILITIES:
- 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.
QUALIFICATIONS:
- Experience: 6+ years working across data science and machine learning (for the Lead level).
- Core Languages: Expert-level SQL and Python.
- Data Types: Hands-on work with both structured and unstructured data, such as tables, text, images, and video.
- Databases: Experience with both relational (SQL) and non-relational (NoSQL) databases.
- Data Processing: Familiarity with ETL and data processing tools like Apache Spark.
- Visualization and Monitoring: Experience using tools to visualize data and monitor its health.
- Engineering Practices: Comfort with modern software practices like automated testing and CI/CD.
- Containers: Experience with Docker.
- Cloud: Experience deploying to GCP or another cloud platform.
BONUS EXPERIENCE:
- Consulting or Professional Services Background
- Domain Knowledge of Healthcare Data
- Knowledge of Healthcare Protocols such as HL7, FHIR, DICOM