Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.
As a Medicare Risk Adjustment AI/ML Engineer, you’ll play a crucial role in supporting the development and enhancement of AI/ML applications related to Medicare risk adjustment, as well as supporting existing AI/ML solutions by consolidating strong engineering and analytical skills.
Upon selection, you will be part of a dynamic team working on developing and delivering best in class analytics for end users. Your work will focus on understanding the CMS Medicare Advantage business and building scalable AI/ML solutions aligned with Business and Technical requirements.
Primary Responsibilities:
Required Qualifications:
- Graduate degree or equivalent experience
- 2+ years of solid experience with Python, Spark, and Hive, and experience developing AI/ML solutions at scale
- 2+ years of experience with AI/ML algorithms (classification/regression, tree models, boosting, etc.)
- Hands on experience building and running AI/ML workloads on Azure Databricks and Azure cloud platforms, leveraging distributed compute for large scale model training and scoring
- Hands-on experience with ML frameworks such as PyTorch and/or TensorFlow
- Hands on experience with Transformer based models such as BERT and its variants for NLP tasks including classification, extraction (NER), semantic similarity, and summarization
- Hands-on experience with supervised learning algorithms including logistic regression, tree based models, random forests, gradient boosting (e.g., XGBoost/LightGBM)
- Experience applying deep learning models (eg., feed forward networks, CNNs, RNNs/Transformers) to structured and/or unstructured data use cases
- Good theoretical and practical knowledge around LLMs and Generative AI and NLP use cases
- Exposure to model packaging/versioning and basic production readiness practices (logging, reproducibility)
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.