Senior Generative AI Scientist I - Cotiviti

Overview

The Sr Gen AI Data Scientist I - Generative AI will apply knowledge and experience to real world problems and seek to utilize their skills reduce the cost of healthcare and improve health quality and outcomes. With access to dedicated on premise and cloud based big data solutions, the team can work with a vast amount of structured and unstructured data including claims, membership, physician demographics, medical records and others to begin to solve some of the most pressing healthcare issues of our time. A Data Scientist at Cotiviti will be given the opportunity to work directly with a team of healthcare professionals including analysts, clinicians, coding specialists, auditors and innovators to set aggressive goals and execute on them with the team. This is for an ambitious technologist, with the flexibility and personal drive to succeed in a dynamic environment where they are judged based on their direct impact to business outcomes.

Please note: We are currently hiring multiple positions at various levels for our Artificial Intelligence teams. Please use the chart below as a guide for the amount of experience required for each level, and apply to the req(s) that are most appropriate for your level of experience.

Responsibilities

As a Sr GenAI Data Scientist I within Cotiviti you will be responsible for delivering solutions that help our clients identify payment integrity issues, reduce the cost of healthcare processes, or improve the quality of healthcare outcomes. You will work as part of a team and will be individually responsible for the delivery of value associated with your projects. You will be expected to follow processes and practices that allow your models to be incorporated into our machine learning platform for production execution and monitoring, however, initial exploratory data analysis allows for more flexible experimentation to discover solutions to the business problems presented.

• Work with key stakeholders within Research and Development as well as Operations, along with product management to assess the potential value and risks associated with business problems that have the potential to be solved using machine learning and Artificial Intelligence techniques.
• Develop an exploratory data analysis approach to verify the initial hypothesis associated with potential Artificial Intelligence/Machine Learning use cases.
• Document your approach, thinking and results in standard approaches to allow other data scientists to collaborate with you on this work.
• Prepare your final trained model and develop a validation test set for QA.
• Work with production operations to deploy your model into production and support them in monitoring model performance.
• Participate in other data science teams collaborating with your peers to support their projects.
• Participate in knowledge sharing sessions to bring new insights