Note: The job is a remote job and is open to candidates in USA. UnitedHealthcare, part of Optum, is working to simplify the health care experience and improve access to quality care. The Principal Data Scientist will lead the architecture, development, and deployment of advanced machine learning and generative AI solutions, while establishing governance, security, reproducibility, and MLOps best practices. The role also includes guiding engineering teams, conducting proof-of-concept experiments, and mentoring junior technical staff.
Responsibilities
- Design, develop, and deploy scalable, production-grade AI solutions to address complex business challenges while embedding responsible AI principles, fairness, transparency, and accountability throughout the model development lifecycle
- Lead solution architecture and hands-on development across complex AI/ML initiatives using traditional ML, deep learning, and modern LLM-based approaches
- Lead proof-of-concept experiments in generative AI (transformers, GANs, diffusion models) and evaluate emerging tools, research, and frameworks to drive strategic innovation
- Collaborate with research, data engineering, software engineering, and product teams to translate cutting-edge AI advancements into scalable, production-ready capabilities
- Define and establish enterprise best practices for model governance, versioning, reproducibility, security, and cloud-native MLOps pipelines
- Provide technical guidance, architectural design reviews, and mentorship to junior engineers and data scientists without formal people management duties
- Document architecture designs, technical proposals, and recommendations, presenting findings directly to cross-functional stakeholders and leadership
Skills
- 10+ years of experience designing, building, and deploying production machine learning solutions
- 3+ years of experience building Generative AI applications using LLMs and orchestration frameworks such as LangChain and/or LangGraph
- Deep expertise in either NLP or Computer Vision with multiple years of hands-on solution ownership in that domain
- Proven solid foundation in core ML and statistical methods (supervised/unsupervised learning, regression, classification, clustering, time series, Bayesian modeling) alongside substantive deep learning experience
- Demonstrated experience with cloud ML services and infrastructure design on at least one major cloud platform (AWS, Azure, or GCP), including containerization (Docker/Kubernetes) and MLOps (CI/CD, model registry, monitoring)
- Proven track record of successfully moving models from research/POC phase into production at scale
- Hands-on programming proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
- Demonstrated solid background in probability, linear algebra, and statistical inferenc
- Demonstrated problem-solving ability with clear verbal and written communication skills
- Experience working with healthcare data, clinical systems, or U.S.-based healthcare environments
- Practical experience fine-tuning generative models (e.g., GPT, BERT, Stable Diffusion, or custom architectures)
- Experience with MLOps tools such as MLflow, Kubeflow, TFX, or Airflow
- Familiarity with big data technologies including Apache Spark, Hadoop, or Dask
- Knowledge of data visualization tools (Tableau, Power BI) and dashboard design
Benefits
- You'll enjoy the flexibility to work remotely from anywhere within the U.S. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.
- A comprehensive benefits package
- Incentive and recognition programs
- Equity stock purchase
- 401k contribution
Company Overview
Company H1B Sponsorship