Manager Data Science

RELX•Raleigh, NC

1d•$115,400 - $192,300

About The Position

We are seeking a Manager of Data Science to lead a high-impact team focused on building intelligent systems powered by LLMs and RAG architectures. This role will focus on improving LLM-powered drafting and retrieval solutions through advanced search, embeddings, reranking, evaluation, and production-grade ML components. You’ll drive the strategy, technical execution, and team growth necessary to build next-generation legal assistants, capable of contextual document drafting, and interaction with structured and unstructured data sources.

Requirements

Previous people management experience (3+ years) of teams building LLM-based applications, especially in domains requiring rigor and traceability like legal, finance, or compliance.
Hands-on experience delivering systems using LangChain, LLM agent frameworks, vector databases, and prompt orchestration pipelines.
Strong background in deploying retrieval-augmented generation (RAG) systems for grounded and auditable responses.
Demonstrated success in taking LLM-powered applications from prototype to production.
Proficient with Python and LLM tooling: LangChain, HuggingFace Transformers, OpenAI APIs, and prompt tuning techniques.
Familiarity with vector databases (e.g., Solr, Elasticsearch, Qdrant, Weaviate), knowledge graphs, and hybrid retrieval architectures.
Working knowledge of containerization (Docker, Kubernetes), CI/CD, and model serving tools (TorchServe, Triton, Ray Serve).
Cloud infrastructure experience on AWS, Azure, or GCP.

Nice To Haves

Graduate degree in Computer Science, AI, Machine Learning, or equivalent experience.
6 years of post-degree experience, with 3+ years in a data science or applied AI leadership role, with a focus on NLP/LLM systems.
Prior experience in legal tech, legal AI, or document-intensive domains is highly desirable.
Familiarity with ethical/legal considerations in deploying generative AI in professional settings.

Responsibilities

Define the roadmap for applying LLMs and agentic AI to real-world legal drafting.
Evangelize and embed AI-driven change across legal product platforms.
Translate evolving generative AI capabilities into tangible customer value within the legal domain.
Architect systems that combine foundation models with RAG pipelines to draft contracts, memos, and pleadings grounded in enterprise knowledge.
Guide the development of AI agents that perform multi-step reasoning, cite evidence, retrieve domain-specific content, and produce compliant outputs.
Mentor and grow a multidisciplinary team of LLM-focused Data Scientists and ML Engineers.
Drive cross-functional collaboration with Legal SMEs, Data Engineers, Product Managers, and Design.
Establish best practices for evaluation, observability, and responsible use of generative AI.