Data Scientist (AI/NLP)

Davis Polk & Wardwell LLP•New York, NY

1d•$200,000 - $240,000

About The Position

Davis Polk & Wardwell LLP (including its associated entities) is an elite global law firm with world-class practices across the board. Clients know they can rely on Davis Polk for their most challenging legal and business matters. From offices in the world's key financial centers and political capitals, our more than 1,000 lawyers collaborate seamlessly to deliver exceptional service, sophisticated advice and creative, practical solutions. Visit davispolk.com. Position Summary We are seeking a talented and driven Data Scientist (AI/NLP). to join our technology team. This is a high-impact, cross-functional role at the intersection of data science, artificial intelligence, and legal operations. You will develop and deploy advanced NLP and machine learning solutions that transform how the firm manages, retrieves, and leverages its institutional knowledge and legal documents. This position partners closely with attorneys, legal professionals, and firm leadership to identify opportunities where AI and data science can deliver measurable value — from automating document workflows to building intelligent knowledge management systems.

Requirements

Direct experience working in a law firm, legal technology company, or enterprise NLP environment
Deep expertise in Natural Language Processing (NLP) — including text classification, named entity recognition, semantic similarity, summarization, and document parsing
Hands-on experience with LLMs and transformer-based architectures (e.g., GPT, BERT, or similar) and proficiency in Hugging Face and LangChain frameworks
Strong proficiency in Python and relevant ML/data science libraries (scikit-learn, PyTorch, spaCy, etc.)
Experience building and deploying end-to-end machine learning pipelines in production environments
Solid understanding of vector databases, embeddings, and retrieval-augmented generation (RAG) architectures
Experience defining and tracking model performance metrics (e.g., precision, recall, F1, ROUGE) to evaluate and communicate model effectiveness to technical and non-technical audiences
Demonstrated ability to manage and process large, unstructured datasets
Familiarity with at least one major cloud platform (Azure, AWS, or GCP)
Excellent communication skills with the ability to work across technical and non-technical teams
Strong analytical and problem-solving ability with a rigorous, detail-oriented approach
Bachelor's or Master's degree in Computer Science, Data Science, Computational Linguistics, Statistics, or a related quantitative field
5–7 years of experience in data science, machine learning, or AI engineering roles

Responsibilities

Design, develop, and deploy machine learning and NLP models to support document understanding, contract analysis, legal research, and knowledge extraction
Build and maintain intelligent knowledge management systems that organize, surface, and operationalize the firm's institutional knowledge and precedent library
Apply state-of-the-art large language models (LLMs) and generative AI tools to legal workflows, including document classification, summarization, entity extraction, and semantic search
Partner with attorneys and business professionals to translate complex legal requirements into structured data science solutions
Write clean, production-quality code and build scalable data pipelines that integrate with firm systems and infrastructure
Evaluate, implement, and continuously improve AI/ML tools, frameworks, and vendor solutions relevant to the legal industry
Define and track model performance metrics, communicate results clearly to both technical teams and business stakeholders, and drive iterative model improvement
Present findings, model outputs, and project updates clearly to both technical and non-technical stakeholders
Contribute to the firm's AI strategy and governance by staying current on developments in legal AI, data privacy, and responsible use of AI
Collaborate with IT infrastructure, security, and application teams to ensure solutions meet firm standards for reliability, security, and compliance