Senior Generative AI Scientist II – Model Risk

This is a fully remote position, open to applicants in United States.

📋 Description

• Provide solutions that assist clients in identifying payment integrity challenges, lowering healthcare process expenses, or enhancing healthcare results.

• Collaborate within a team while taking individual responsibility for delivering project value.

• Perform independent model validation for benchmarking, assessment, and evaluation of effectiveness.

• Identify model drift and data drift as part of model risk management.

• Utilize expertise in AI/ML/GenAI model development, creation, and assessment.

• Benchmark and potentially reconstruct existing models using updated data and advanced algorithms.

• Promote enhancements in model monitoring, registration, metadata management, and associated tools and techniques.

• Achieve performance review objectives, special projects, and other assigned tasks.

• Collaborate with global teams and US-based teams that are geographically distributed.

⛳️ Requirements

• Master's degree in a quantitative field such as Computer Science/Engineering, Statistics, or Operations Research, focusing on Advanced Statistics, Machine Learning, and AI.

• Over 5 years of practical experience in data science/AI.

• Proficient in natural language processing methods, including transformers, fine-tuning LLMs, and metrics for measuring/benchmarking and deploying LLMs.

• Familiarity with tools such as HuggingFace, Langchain, LLAMA/Mistral, OpenAI, and vector databases.

• Proficient in using pandas, scikit-learn, keras, nltk, TensorFlow/PyTorch, and GPU technologies.

• General knowledge of Responsible AI, explainability, AI NIST RMF, and related AI risk management frameworks.

• Experience assessing models for bias and fairness, including identifying bias in model design and data.

• Skilled in applying metrics like SHAP and LIME.

• Understanding of model metrics and methodologies for managing, assessing, and monitoring GenAI models and LLMs.

• Experience in building production-grade machine learning applications on AWS, Azure, or GCP.

• Experience with Apache Spark and large-scale distributed datasets.

• Ability to ensure high-speed internet access/connectivity along with an organized office setup and maintenance.

• Capability to provide a dedicated and secure work environment.

• Must be able to perform responsibilities with or without reasonable accommodation.

• Familiarity with the healthcare payor ecosystem and related data is preferred.

• Understanding of best practices in model governance and data governance is preferred.

• Experience with Python, DataRobot, AWS Sagemaker, DataBricks, AWS Model Monitor, MLFlow, NannyML, FiddlerAI, and Arize is preferred.

🏝️ Benefits

• Coverage for medical, dental, vision, disability, and life insurance.

• 401(k) savings plans.

• Paid family leave.

• Nine paid holidays annually.

• 17-27 days of Paid Time Off (PTO) each year, depending on specific level and tenure.

• Option for remote work.

• Virtual interview process.

• Reasonable accommodations provided where applicable.