Senior Generative AI Scientist II

Responsibilities

• Deliver solutions that help clients identify payment integrity issues, reduce healthcare costs, or improve healthcare outcomes.
• Work as part of a team and be individually responsible for the delivery of value associated with your projects.
• Follow processes and practices allowing your models to be incorporated into the Cotiviti machine learning platform for production execution and monitoring.
• Assess the potential value and risks associated with business problems that can be solved using machine learning and artificial intelligence techniques.
• Develop an exploratory data analysis approach to verify initial hypotheses associated with potential AI/ML use cases.
• Document your approach, thinking, and results in standard approaches to allow collaboration with other data scientists.
• Prepare your final trained model and develop a validation test set for quality assurance.
• Deploy your model into production and support monitoring model performance with ML Ops/Production operations.
• Participate in design sessions to continuously develop and improve the Cotiviti machine learning platform.
• Collaborate with peers to support their projects and participate in knowledge sharing sessions.
• Provide end-to-end value-based solutions, including data pipelines, model creation, and application for end-user consumption.
• Complete all responsibilities as outlined in the annual performance review and/or goal setting.
• Complete special projects and other duties as assigned.

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 like HuggingFace, Langchain, LLAMA/Mistral, and OpenAI, and vector databases.
• 5+ years of hands-on data science/AI experience, using typical machine learning and data science tools such as 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 technical audiences is a plus.
• Flexibility to work with global teams and geographically dispersed US-based teams.
• Professional with the ability to properly handle confidential information.
• Able to work within a matrixed organization.
• Proficiency in all required skills and competencies above.