Sr. Data Scientist, Applied Analytics and Production Solutions - Remote

UnitedHealth Group•Washington, DC

8d•$91,700 - $163,700•Remote

About The Position

Optum's Enterprise Clinical Services team is seeking a Senior Data Scientist to lead the strategy, architecture, implementation, scaling, and continuous improvement of applied AI and natural language processing solutions that improve healthcare delivery and operational efficiency. This role provides accountable technical leadership for NLP, large language models, voice and call analytics, AI-enabled quality assurance, intelligent automation, and related advanced analytics capabilities. The position combines hands-on technical execution, solution architecture, business strategy, and team leadership. Working at the intersection of data science, engineering, business intelligence, and clinical operations, you will move ambiguous business needs through research, design, deployment, monitoring, adoption, and sustained production use. You will also help shape the capability roadmap, establish reusable technical patterns and evaluation practices, and mentor other data scientists. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. 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.

Requirements

4+ years of experience in analytics or data science
3+ years of experience applying machine learning, natural language processing, optimization, operations research, or related advanced analytical techniques to business problems
3+ years of experience creating dashboards or business intelligence solutions using Power BI, Tableau, or similar tools
2+ years of experience with data engineering, data modeling, database design, query optimization, and SQL development
2+ years of experience designing and implementing data pipelines, automated workflows, software applications, or production analytical services
Experience leading projects, conducting technical reviews, and mentoring team members
Demonstrated experience designing and implementing production NLP, Generative AI, LLM, voice analytics, or other AI-enabled solutions
Advanced proficiency in Python and SQL, including experience with data manipulation, model development, testing, and production-quality code
Proven ability to lead complex analytical initiatives from problem definition through implementation and adoption
Demonstrated solid strategic thinking, problem-solving, written, and verbal communication skills, including presenting recommendations to senior leaders and non-technical stakeholders
Demonstrated ability to navigate ambiguity, understand end-user needs, and translate business opportunities into practical technical solutions

Nice To Haves

Hands-on experience with LLM application patterns such as prompt engineering, retrieval-augmented generation, embeddings, semantic search, evaluation, guardrails, or model monitoring
Experience with Snowflake for data access, transformation, analytical workloads, or production data pipelines
Experience with Microsoft SQL Server and cloud-based analytics or AI platforms
Experience with AI-enabled automation, contact-center analytics, voice analytics, or operational monitoring
Experience establishing model-evaluation practices, technical standards, governance approaches, or production-readiness criteria for AI solutions
Experience administering analytics infrastructure, databases, job-scheduling tools, and production application environments
Experience in healthcare analytics or healthcare operations
Experience in logistics, workforce optimization, routing, scheduling, operations research, supply chain, or Geographic Information Systems (GIS)
Experience influencing business strategy through analytics and advanced technology

Responsibilities

Applied AI, NLP, and LLM leadership
Establish technical direction for NLP, large language model, voice analytics, and applied AI capabilities
Evaluate emerging AI technologies and identify practical applications for clinical and business operations
Design and implement AI-enabled operational solutions, including voice and call analytics, AI-driven monitoring, and intelligent workflow automation
Build scalable frameworks, reusable components, and solution patterns that accelerate future AI development and deployment
Establish technical standards, evaluation methods, governance approaches, and best practices for AI-enabled capabilities
Guide the responsible transition of AI concepts, research, and prototypes into reliable, supported production solutions
Advanced solution development and production stewardship
Lead complex analytical initiatives from problem definition through implementation, adoption, performance monitoring, and enhancement
Translate ambiguous business challenges into structured analytical approaches, technical architectures, and executable solution plans
Design and oversee scalable data pipelines, feature and data-processing workflows, application architectures, and automation frameworks
Develop and apply NLP, LLM, machine learning, statistical, optimization, and operations research methods to business problems
Define monitoring, validation, reporting, documentation, and operational-readiness expectations for production AI solutions
Lead technical troubleshooting and root-cause analysis for complex model, data, and application issues
Review models, prompts, code, analytical methods, architecture, and implementation approaches for quality, reliability, and maintainability
Business leadership and team development
Partner with senior leaders and business stakeholders to identify high-value opportunities and translate strategy into practical solutions
Communicate technical concepts, model results, limitations, risks, and trade-offs clearly to technical and non-technical audiences
Shape the roadmap for NLP, LLM, voice analytics, automation, and advanced analytical initiatives
Mentor GL27 Data Scientists and other technical contributors, delegate work effectively, and strengthen cross-training and knowledge transfer
Develop executive-level presentations, dashboards, decision-support materials, technical documentation, and knowledge-sharing resources
Balance near-term delivery and production stewardship with longer-term capability development