Machine Learning Engineer II (Remote)

Availity delivers revenue cycle and related business solutions for health care professionals. The Machine Learning Engineer will support the goal of connecting providers, payers, and patients to the Internet of Healthcare by using AI and machine learning to improve processes and patient treatment outcomes.

Responsibilities:

• Support our goal of connecting providers, payers, and patients to the Internet of Healthcare
• Use Natural Language Processing and Artificial Intelligence/Machine Learning toolkits to build solutions to improve this process and get patients the treatment they need sooner
• Develop machine learning architectures to make Availity better at determining prior authorization through Machine Learning Operations practices
• Design experiments to test architectures; and deploy selected models as service endpoints
• Monitor models in production; update models as needed to improve performance; and automate processes where possible
• Understand and maintain data pipelines from raw sources to feature stores for models
• Communicate within the team on project status and blockers to get help when needed and render aid when able
• Adhere to security and data protection policies when performing the above duties

Requirements:

• Master's degree in Data Science, Data Analytics, Business Analytics and Information Systems or a directly related field
• 3 years of experience as a data scientist, machine learning engineer or related occupation
• 2 years of experience with designing and deploying end-to-end regression and classification models on AWS SageMaker
• 2 years of experience with Databricks using scikit-learn and PySpark ML, including techniques for handling highly imbalanced datasets
• 2 years of experience with Natural Language Processing
• 2 years of experience with Hugging-Face with PyTorch to process unstructured text, generate dense embeddings for text understanding applications
• 2 years of experience with healthcare data standards, including EDI 835/837 and medical coding systems, including ICD-10, CPT, HCPCS, and SNOMED-CT, and secure handling of PHI and PII
• 1 year of experience with developing and deploying RESTful web APIs for ML model inference using FastAPI, Flask, and Django frameworks
• 1 year of experience with Docker containerization to create reproducible and scalable environments for machine learning models and cloud native deployment using Amazon ECS and EKS
• 1 year of experience with writing queries using PySpark on Databricks to retrieve and process semi-structured data