Legal Data Intelligence Lead

Job Responsibilities

• Contribute to Relativity applied science strategy by leveraging your subject matter expertise to design, build and improve our tests, benchmarks, and other evaluation systems for our generative AI solutions.
• Work internally with our applied scientists, product managers, designers, engineers, and customer enablement teams to provide internal feedback and suggestions on the enablement, performance, usability, and functionality of our generative AI solutions.
• Work externally with our clients and our industry to explain how our generative AI systems work and to receive their feedback on the performance of our generative AI in analyzing and classifying documents, identifying information that needs to be protected, and extracting and curating knowledge.
• Develop and deliver training and documentation for internal teams on legal data intelligence workflows and best practices.

Minimum Qualifications

• A Juris Doctorate and at least 3-5 years of experience as a practicing attorney; some but not all of this requirement may be satisfied by equivalent experience working for a legal service provider or legal technology vendor.
• Deep interest in the use of generative AI to responsibly advance the practice of law and access to justice.
• Strong and clear communicator, able to synthesize and communicate complex technical concepts and results to internal and external audiences.
• Experience in discovery or legal technology platforms.
• Experience working with cross-functional teams.

Preferred Qualifications

• Significant litigation experience and deep understanding of the processes of investigations and litigation, including discovery and pre-trial practice.
• At least 5 years of experience as a practicing attorney.
• Strong understanding of the traditional EDRM and broader Legal Data Intelligence use cases and workflows.

Required Skills

• Algorithms
• Data Analysis
• Machine Learning (ML)
• Natural Language
• Python (Programming Language)
• Reinforcement Learning
• Researching
• Scientific Writing
• Statistical Models
• Technical Leadership