Senior Data Scientist - GenAI

CVS Health•New York, NY

1d•$111,240 - $222,480

About The Position

CVS Health is seeking a Senior Data Scientist - GenAI to join Lumina, its enterprise knowledge and agentic AI platform team. The role advances the intelligence, scalability, reliability, and business impact of AI-powered experiences used across the enterprise. It combines applied data science, machine learning, information retrieval, Generative AI, and software engineering to deliver production-grade solutions at scale. The position contributes across the full AI product lifecycle, from research and prototyping through deployment, monitoring, optimization, and continuous improvement. The work uses experimentation, statistical methods, machine learning, and large language models to improve relevance, accuracy, user satisfaction, and operational efficiency. Approximately two-thirds of the role focuses on applied data science, machine learning, Generative AI, experimentation, and evaluation; approximately one-third focuses on software engineering, platform integration, API development, and production implementation. Lumina grounds AI responses in curated internal content and enforces access controls through Azure Entra security groups. It delivers capabilities through web applications, Slack, Microsoft Teams, embedded agents, APIs, and Model Context Protocol integrations, and supports secure interoperability with systems including Jira, Confluence, GitHub, Figma, Airtable, Slack, and Microsoft 365. The Senior Data Scientist collaborates with software and platform engineers, architects, product managers, and business leaders to deliver enterprise-scale AI capabilities.

Requirements

At least 5 years applying advanced data science, machine learning, artificial intelligence, statistical modeling, and quantitative methods to complex business challenges in large-scale production environments.
At least 4 years building and maintaining production-grade software, services, APIs, data pipelines, and application components using Python and modern engineering practices.
At least 3 years leveraging AWS, Azure, or GCP for scalable analytics, machine learning workloads, distributed applications, and cloud-native platforms.
At least 2 years designing, deploying, and optimizing Generative AI, LLM, RAG, agentic AI, or intelligent automation solutions in enterprise environments.
At least 1 year establishing evaluation frameworks, experimentation methodologies, and performance measurement practices that drive measurable business outcomes and informed technology decisions.
Bachelor’s degree from an accredited university or equivalent work experience; the stated equivalent is a High School Diploma/GED plus 4 years of relevant experience.

Nice To Haves

Experience supporting enterprise AI platforms with governance, security, scalability, reliability, and operational maturity requirements.
Experience implementing observability, AI quality measurement, performance monitoring, operational analytics, or continuous improvement capabilities for machine learning and Generative AI applications.
Familiarity with enterprise search, knowledge management, information discovery, content intelligence, or access-controlled information ecosystems.
Experience in healthcare, regulated industries, or environments where privacy, compliance, risk management, and security controls are critical.
Master’s degree in Data Science, Computer Science, Statistics, Artificial Intelligence, Machine Learning, or a related quantitative discipline.

Responsibilities

Design, build, and evaluate retrieval and generation components, including embeddings, hybrid retrieval, reranking, prompting, and agent behavior, for an enterprise RAG and agentic AI platform.
Develop and maintain scalable evaluation methods for retrieval quality, including Recall@k, MRR, and nDCG, and response quality, including faithfulness, citation accuracy, and abstention, using production traces and human-validated judgments.
Analyze usage, performance, and quality data to inform product and modeling decisions, and communicate findings to technical and business stakeholders.
Create and orchestrate agents that assist with platform development, internal processes, and customer support, and help advance autonomous AI systems.
Write production-level code to integrate models and evaluation pipelines into agent orchestration, retrieval, and data layers.
Build supporting APIs and connectors and contribute to software development and platform integration.
Contribute across AI product development, including research, prototyping, deployment, monitoring, optimization, and continuous innovation.
Build and deploy ML microservices; implement unit, integration, and contract testing; and support automated testing and deployment pipelines.