Sr Generative AI Scientist II - (Model Risk)

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.

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.
• 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.

Cognitive / Mental Requirements

• Ability to work independently as well as collaborate as a team with a sense of urgency.
• Professional with ability to properly handle confidential information.
• Be value-driven, understand that success is based on the impact of your work rather than its complexity or the level of effort.
• Ability to handle multiple tasks, prioritize and meet deadlines.
• Ability to work within a matrixed organization.
• Proficiency in all required skills and competencies above.
• Communicating with others and teamwork.
• Assessing the accuracy, neatness, and thoroughness of the work assigned.

Physical Requirements and Working Conditions

• Flexibility to work with global teams as well geographically dispersed US based teams.
• Remaining in a stationary position, often standing or sitting for prolonged periods.
• Repeating motions that may include the wrists, hands, and/or fingers.
• Must be able to provide high-speed internet access/connectivity and office setup and maintenance.
• Must be able to provide a dedicated, secure work area.

Base compensation ranges from $153,000 to $180,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.

Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our Careers page.

Since this job will be based remotely, all interviews will be conducted virtually.

Date of posting: 9/16/2026

Applications are assessed on a rolling basis. We anticipate that the application window will close on 12/16/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.

#senior

• 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, DataRobot, AWS Sagemaker), model deployments (AWS, Azure, DataRobot, DataBricks), model monitoring (AWS Model Monitor, MLFlow, NannyML, FiddlerAI, Arize) and related tools for model management and metadata management.