Optum is looking to solve the problem of improving health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best, through the use of AI/ML solutions
Requirements
- PhD in AI, computer science, data science, applied math or related technical fields
- Advanced understanding of Deep Learning with applications in NLP and multimodal modeling
- Proven solid Python programming skills and experience with deep learning frameworks such as PyTorch or TensorFlow
- 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: GNNs, GANs, Transformers, deep and wide, SSMs, LLMs among others
- Proven in-depth knowledge in Generative AI technology and Large Language Models (LLM) with focus in LLM optimization
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
- Mentor and help recruit AI/ML scientists and machine learning engineers to the team
Other
- 10+ years of industry/academic experience in machine learning or a related field
- 5+ years of building models for business application experience
- 5+ years leading technical teams
- PhD in AI, computer science, data science, applied math or related technical fields
- UnitedHealth Group complies with all minimum wage laws as applicable
