Innovation Data Scientist

Location: New York

About Kirkland & Ellis

At Kirkland & Ellis, we don't just meet the standard for legal excellence - we set it. Our culture is built on teamwork, ingenuity and an unwavering commitment to continuous growth. We tackle the most sophisticated legal challenges with bold ideas and innovative solutions, powered by the exceptional experience and ambition of our 7,000+ people, including 4,000+ attorneys, across 23 offices worldwide.

Our dedicated professionals share our lawyers' commitment to excellence and show up each day to do meaningful work that helps drive global business, investment and innovation forward.

What You'll Do

Are you energized by applying advanced analytics and artificial intelligence (AI) to transform how a leading law firm delivers client service and drives business performance? As an Innovation Data Scientist within Innovation Engineering at Kirkland & Ellis, you'll design and deploy intelligent, data-driven solutions that enhance quality, speed, and decision-making across legal work and firm operations.
Partnering closely with attorneys, innovation leaders, and enterprise teams, you'll translate complex legal and business challenges into secure, scalable AI and machine learning (ML) solutions. From experimentation and measurement to deployment and monitoring, you'll lead the full solution lifecycle-curating and governing datasets, defining success metrics for accuracy and safety, and building production-ready models and applications, including large language model (LLM)-powered and classical ML solutions.
Reporting to the Director of Innovation Engineering, you'll play a key role in shaping and scaling the firm's data science program-automating routine work, surfacing actionable insights, and unlocking new capabilities that elevate both client outcomes and firm performance.

Translate Legal Workflows into AI Solutions - Lead discovery sessions with attorneys and practice stakeholders to convert end-to-end legal processes into clear data and LLM requirements, measurable success criteria, and evaluation plans aligned to business value.
Design and Prototype Intelligent Applications - Build and evaluate AI/ML techniques, including retrieval-augmented generation (RAG) pipelines, vector database integrations, and orchestration frameworks to assess feasibility, performance, and scalability.
Deliver High-Impact Legal Use Cases - Develop solutions for contract analysis, due diligence review, document summarization, large-scale agreement comparisons, and other practice-driven needs.
Establish Evaluation & Governance Frameworks - Define and operationalize production-ready evaluation standards, measuring accuracy, robustness, bias and fairness, hallucinations, and business-aligned performance thresholds with ongoing monitoring.
Develop Reusable Assets & Libraries - Create internal libraries, documentation, and repeatable development patterns that enable efficient and scalable AI solution delivery.
Collaborate Across Technical & Legal Teams - Partner with embedded data science teams and business stakeholders to align priorities, co-develop solutions, and ensure successful adoption across practice groups.
Communicate Insights with Clarity - Present evaluation results, trade-offs, risks, and expected impact in clear, accessible language to non-technical stakeholders, including attorneys and business leaders.
Advance Innovation Strategy - Evaluate emerging tools, open-source libraries, and vendor platforms to inform technology strategy, contribute to internal thought leadership, and support firmwide AI enablement initiatives.
Mentor & Enable Growth - Support and guide data scientists and engineers, fostering knowledge sharing and strengthening the firm's AI capabilities.

What You'll Bring

Education - bachelor's degree in computer science, statistics, mathematics, engineering, or a related quantitative field; advanced degree preferred.
Experience - 7+ years of experience delivering AI/ML solutions in professional services, financial services, legal, or technical environments, with substantial cross-functional collaboration involving legal and business stakeholders.
Technical Expertise - Strong Python skills for data analysis, statistical modeling, and AI/ML prototyping (e.g., pandas, PyTorch, Tensor Flow); advanced SQL proficiency; experience building data pipelines and preparing structured and unstructured datasets.
AI & LLM Application Knowledge - Hands-on experience with AI platforms and tools (e.g., OpenAI, Microsoft Azure OpenAI, Hugging Face, Lang Flow, Lang Chain), vector databases, retrieval methods for unstructured data, and knowledge-graph-backed applications.
Production AI Evaluation - Demonstrated experience defining and implementing evaluation frameworks for enterprise AI solutions, including monitoring for accuracy, robustness, hallucinations, bias, and fairness.
Visualization & Communication - Familiarity with data visualization tools (e.g., Plotly, Tableau, Microsoft Power BI) and the ability to translate complex…