Revecore is looking to drive its AI-enabled workflow program and enhance the productivity and efficiency of its underpayment business by leveraging machine learning, data analysis, and software engineering.
Requirements
- 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.
- Experience with scikit-learn, TensorFlow, PyTorch, in Python and/or Scala.
- Ability to wrangle data, perform exploratory data analysis, and draw insights from visualizations.
- Applied experience with contemporary natural language processing (NLP) tasks, techniques, and tools (e.g., entity extraction, seq2seq modeling, transformers, hugging face)
- Experience with Spark
- Experience with operating ML pipelines in a cloud platform (e.g., AWS, GCP, Azure)
Responsibilities
- Own end-to-end development, training, productionization, 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 MLOps 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.
Other
- Bachelor's degree in Computer Science, Data Science, Mathematics, or a related field.
- A quiet, distraction-free environment to work from in your home.
- A secure home internet connection with speeds >20 Mbps for downloads and >10 Mbps for uploads is required.
- Must reside in the United States within one of the listed states
- Travel requirements not specified
