Sr Generative AI Scientist II Model Risk & Validation

Overview

Join a recently formed team focused on Model Risk and Responsible AI. The Senior GenAI Scientist II – Risk will apply knowledge and experience to real world problems and seek to utilize their skills to reduce the cost of healthcare and improve health quality and outcomes. As a Data Scientist on this team, you will focus on three main project areas:
Model Validation, Model Metrics and Monitoring, and Responsible AI. This requires someone with depth in AI/ML/GenAI from a data science perspective, versatility to think in terms of technology systems, and some understanding of emerging areas of Responsible AI and AI Ethics. 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.

• As a Senior GenAI Scientist II 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.
• Conduct independent model validation of existing models for benchmarking, assessment, and gauging effectiveness
• Determine aspects of model drift and related data drift for the purpose of model risk management (MRM) to both reduce risk and also find opportunities to drive new revenue growth and innovation
• Apply deep expertise with AI/ML/GenAI model development, including hands‑on experience with model building and model evaluation
• Benchmark and potentially rebuild existing models as needed using updated data, and potentially newer, more modern and effective algorithms
• Actively drive improvements in model monitoring activities, including methods for model registration, model metadata management, and conceptualizing approaches for related tools and techniques
• 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.

• 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 Hugging Face, 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 Tensor Flow/PyTorch, GPU.
• General understanding of Responsible AI (RAI), including explainability (XAI), AI NIST RMF, and related AI risk management frameworks.
• Experience and understanding evaluating models for bias and fairness, with aptitude for detecting bias in the model design and data, as well as using metrics such as SHAP and LIME.
• Understanding appropriate model metrics and techniques for managing, evaluating and monitoring GenAI models and LLMs
  · 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. Passion for collaboration, learn it all mindset and driving value with AI.

• Familiarity with healthcare payor ecosystem and related data.
• Understanding and familiarity with model governance and data governance best practices.
• Strong understanding of technology systems for model development (e.g., Python, Data…