Optum AI is looking to develop and deploy AI/ML solutions for high-impact opportunities across UnitedHealth Group businesses, aiming to transform the healthcare journey through responsible AI/ML innovation and improve health equity on a global scale.
Requirements
- 5+ years of Python programming skills and experience with deep learning frameworks such as PyTorch or TensorFlow
- Advanced understanding of Deep Learning with applications in Time series Processing, NLP and multimodal modeling
- Experienced mathematical background, especially in optimization theory, stochastic algorithms, and/or numerical methods
- Experience with state-of-the-art algorithms and topologies including, but not limited to: VAR, GNNs, GANs, Transformers, deep and wide, SSMs, LLMs among others
- Proven in-depth knowledge in Generative AI technology and Large Language Models (LLM)
Responsibilities
- Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale and complexity
- Build Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing the ML models
- Perform hands-on analysis and modeling of healthcare data sets to develop insights that increase business value
- Run A/B experiments, gather data, and perform statistical analysis
- Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving
- Research and implement innovative machine learning approaches
- Be the thought leader in cutting-edge AI research and proactively pursue IP submissions
Other
- PhD in AI, computer science, data science, applied math or related technical fields
- 7+ years of industry/academic experience in machine learning or a related field
- 5+ years of building models for business application experience
- Mentor and help recruit AI/ML scientists and machine learning engineers to the team
- Present findings and insights to senior leadership and at internal venues
