Senior Generative AI Scientist II

Overview

The Sr Data Scientist - Generative AI II will apply knowledge and expertise to real world problems to enable healthcare organizations to deliver better care at lower cost through advanced technology and data analytics, helping to ensure the quality and sustainability of how healthcare is delivered in the United States. 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 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 on 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 Scientist 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 Business 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 ML Ops/Production operations to deploy your model into production and support them in monitoring model performance.
• Participate in design sessions to continuously develop and improve the Cotiviti machine learning platform.
• Participate in other data science initiatives, collaborating with your peers to support their projects.
• Participate in knowledge sharing sessions to bring new insights and technologies to the team.
• Provide End to End value-based solutions, including data pipeline, model creation and application for end user consumption.
• Complete all responsibilities as outlined in the annual performance review and/or goal setting.
• Complete all special projects and other duties as assigned.
• Must be able to perform duties with or without reasonable accommodation.

This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change.  

Qualifications

• Graduate degree in a quantitative discipline such as Computer Science/Engineering, Statistics, Operations Research covering Advanced Statistics, Machine learning and AI.
• Experience with the latest techniques in natural language processing including transformers, fine-tuning LLMs, measuring/benchmarking and deploying LLMs with tools such as HuggingFace, Langchain, LLAMA/Mistral and OpenAI, vector databases.
• 5+ years of hands-on data science/AI experience, using typical machine learning and data science tools including pandas, scikit-learn, keras, nltk, and TensorFlow/PyTorch, GPU.
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP.
• Experience working with Apache Spark™ and large-scale distributed datasets.
• Experience communicating technical concepts to non-technical and tec