Senior Data Scientist

Overview

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.

Optum's Applied AI team is seeking a detail-oriented, innovative, and proactive Senior Data Scientist with 3-5 years of hands-on industry experience in machine learning (ML) and natural language processing (NLP). In this role, you will help design, develop, and maintain document intelligence platforms that power multiple product streams across the organization.

As a Senior Data Scientist, you will collaborate closely with data engineers, domain experts, and fellow data scientists to unlock large-scale data capabilities. You will work with both structured and unstructured clinical datasets, applying advanced algorithms and state-of-the-art modeling techniques to build, optimize, and deploy scalable ML/NLP solutions in production environments.

Your contributions will directly support the creation of high-impact AI solutions that improve healthcare operations and outcomes, enabling smarter insights from complex clinical documentation. This role offers the opportunity to work at the intersection of healthcare and cutting-edge AI, shaping the future of intelligent document processing at scale.

Primary Responsibilities

• Design, develop, and deploy advanced AI solutions for healthcare, including multi-modal document understanding modules, large language models (LLMs) for clinical reasoning, vision-language models (VLMs), and large-scale computer vision/NLP systems (e.g., handwriting recognition, forms processing, named entity recognition, negation detection, terminology disambiguation)
• Own the end-to-end machine learning lifecycle - from problem identification and scoping, data exploration, annotation pipeline creation, and model prototyping to training, deployment, monitoring, and iterative improvement
• Implement intelligent information extraction and retrieval systems, including semantic search, entity linking, and human-in-the-loop pipelines with real-time feedback mechanisms
• Build and maintain scalable ML infrastructure capable of millions of daily predictions, leveraging asynchronous inference, streaming data pipelines, GPU auto-scaling, and modular microservice deployment stacks with CI/CD, telemetry, and monitoring
• Collaborate closely with healthcare domain experts to ensure solutions are clinically accurate, safe, and compliant with industry regulations, and partner with software engineers and ML infrastructure teams to integrate models seamlessly into production environments
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
  
  

• 4+ years of professional experience in machine learning, applied AI, or data science roles, with a solid track record of delivering production-grade solutions
• Hands-on expertise with transformer-based architectures (e.g., BERT, GPT, Vision-Language Models) and experience fine-tuning and optimizing them for domain-specific tasks
• Experience with the full Python ML stack (NumPy, Pandas, scikit-learn, etc.) for experimentation and data analysis
• Proficiency in PyTorch, Python, and core data processing libraries, with solid SQL skills for data extraction and manipulation
• Proven experience in GPU-based deployment of ML models, including optimization for inference speed and cost efficiency
• Proven solid background in natural language processing (NLP) - building, training, and deploying models at scale for tasks such as NER, text classification, semantic search, and document understanding
• Proven skilled in model optimization techniques (quantization, distillation, pruning) to improve performance in production environments
  
  

• Data Quality Monitoring: Experience with tools or techniques for detecting data drift or label inconsistencies
• Healthcare/NLP Domain Knowledge: Prior work with clinical documents, EMR data, or coding workflows
• Familiarity with ML deployment pipelines and MLOps practices, including CI/CD, containerization, and monitoring
• Familiarity with Annotation Tools: Prodigy, Label Studio, or custom annotation platforms
• Cloud Exposure: Basic familiarity with AWS ecosystem
• Proven excellent problem-solving skills, with the ability to work cross-functionally with engineers, domain experts, and product teams
• Visualization Tools: Power BI, Tableau, or Plotly for dashboarding and reporting
  
  

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