Key skills
Machine learningExploratory data analysisModel deploymentPythonscikit-learnTensorFlowPyTorchData wranglingData visualizationNatural language processingEntity extractionTransformersHugging FaceSparkCloud ML pipelinesAWSGCPAzureScientific thinkingAIMachine LearningMLNLPNatural Language ProcessingMLOps
About this role
Revecore is a company that has been at the forefront of specialized claims management for over 25 years, helping healthcare providers recover more revenue. As a Senior Machine Learning Engineer, you will enhance the productivity and efficiency of the underpayment business by developing and deploying machine learning systems to prioritize claims and improve processes.
Responsibilities:
- Own end-to-end development, training, deployment, evaluation, and improvement of machine learning systems to rank claim opportunities
- Analyze and explore data to identify actionable opportunities from internal and 3rd party data
- Research, implement, and launch new model architectures that drive business impact
- Partner and collaborate with cross-functional teams of software engineers, data engineers, subject matter experts, product managers, and analysts to design and build practical solutions
- Implement cloud MLOps and AIOps best practices to streamline the development, deployment, and maintenance of machine learning models
- Continuously measure the impact of the AI-enabled workflows on key business metrics and use these measurements to improve the machine learning models and workflows
- Learn from and teach your teammates. You will be the team’s expert in your specialization, and you will learn from experts in theirs
- Own a workstream. You’ll be the technical lead for the workstream, partnering with others to deliver. You’ll also work on other projects, but this workstream will be one of your key successes
Requirements:
- A bachelor's degree in any data-centric field
- Experience working in a similar role, with a focus on machine learning or data science
- Experience developing and deploying machine learning models in a production environment
- Strong experience with Python, including scikit-learn
- Ability to wrangle data, perform exploratory data analysis, and draw insights from visualizations
- Intuition about data developed by doing statistics and/or research
- Applied experience with contemporary natural language processing (NLP) techniques and tools (e.g., entity extraction, transformers, Hugging Face)
- Experience with Spark
- Experience with operating ML pipelines in a cloud platform (e.g., AWS, GCP, Azure)
- A master's degree, Ph.D., or other experience demonstrating scientific thinking