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 across CVS Health. It combines applied data science, machine learning, information retrieval, Generative AI, and software engineering to deliver production-grade solutions at enterprise scale. The position leads work to improve how enterprise knowledge is discovered, interpreted, and used through AI techniques, intelligent retrieval systems, and autonomous workflows. It applies experimentation, statistical methods, machine learning, and large language models to improve relevance, accuracy, user satisfaction, and operational efficiency. The role contributes throughout the AI product lifecycle, from research and prototyping to deployment, monitoring, optimization, and continuous innovation, working with engineering, architecture, product, and business colleagues. Approximately two-thirds of the role focuses on applied data science, machine learning, Generative AI, experimentation, and AI 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 provides capabilities through web applications, Slack, Microsoft Teams, embedded agents, APIs, and Model Context Protocol (MCP) integrations, and supports secure interoperability with enterprise systems including Jira, Confluence, GitHub, Figma, Airtable, Slack, and Microsoft 365.

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 using 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; equivalent experience is defined as a high school diploma/GED plus 4 years of relevant experience.
Experience with ML microservices, unit testing, integration testing, contract testing, and automated CI/CD testing and deployment pipelines.

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 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 with critical privacy, compliance, risk management, and security requirements.
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, such as 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 to assist with platform creation, internal processes, and customer support, and help develop autonomous AI systems.
Write production-level code to integrate models and evaluation pipelines into platform agent orchestration, retrieval, and data layers.
Build supporting APIs and connectors and integrate AI capabilities into production environments.