Join Optum, a global leader dedicated to improving health outcomes through innovative technology. We connect individuals with essential care, pharmacy benefits, data, and resources, fostering a healthier world. Our inclusive culture champions collaboration with talented peers, offers comprehensive benefits, and provides abundant career development opportunities. Contribute to advancing health optimization globally and experience our commitment to Caring, Connecting, and Growing together.

This role focuses on leveraging AI and ML to solve complex business challenges and enhance health outcomes.

Design, develop, and deploy advanced machine learning models for predictive analytics, forecasting, recommendation systems, classification, and anomaly detection. Translate business needs into scalable AI/ML solutions, applying statistical modeling and data science techniques. Conduct exploratory data analysis, feature engineering, and data preparation for model experimentation. Build and maintain robust ML pipelines covering data ingestion, training, validation, deployment, and monitoring.

Implement MLOps practices for seamless model deployment and lifecycle management, including experiment tracking, versioning, and CI/CD integration. Ensure model reliability through continuous monitoring for accuracy, drift, latency, and operational health. Collaborate with data engineering teams to create scalable data pipelines and integrate AI capabilities into enterprise applications and APIs.

Uphold Responsible AI principles focusing on explainability, fairness, and transparency. Support model validation and compliance activities, adhering to organizational security and privacy standards. Explore emerging AI technologies like Generative AI, LLMs, and RAG to drive innovation and contribute to reusable AI components and frameworks.

A Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Artificial Intelligence, or a related field is required. Possess a minimum of 6 years of experience in Machine Learning, Artificial Intelligence, Data Science, Software Engineering, or a closely related discipline.

Demonstrated experience in developing and deploying ML solutions within enterprise or cloud environments, including building ML pipelines and production-ready AI systems. Proficiency in working with APIs, cloud-based AI services, and distributed data platforms is essential. Experience integrating AI/ML solutions into business applications and workflows, coupled with a strong understanding of model monitoring, performance evaluation, and production support processes, is expected.

Solid analytical, problem-solving, communication, and collaboration skills are crucial. A foundational understanding of Responsible AI, model governance, and compliance requirements is necessary. Expertise in Machine Learning, Statistical Modeling, Predictive Analytics, Model Evaluation, and Feature Engineering is required. Familiarity with MLOps practices, including deployment, monitoring, experiment tracking, and lifecycle management, is key. Strong programming skills in Python and SQL are mandatory.