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
Develop machine learning architectures to make Availity better at determining prior authorization
Design experiments to test architectures and deploy selected models as service endpoints
Monitor models in production and update models to improve performance
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

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
2 years of experience with Natural Language Processing
2 years of experience with Hugging-Face with PyTorch to process unstructured text and 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 for scalable environments for machine learning models and cloud native deployment using Amazon ECS and EKS
1 year of experience writing queries using PySpark on Databricks to retrieve and process semi-structured data

Video participation is required for virtual meetings to enhance security and communication.

Machine Learning Engineer

Key skills