ZipRecruiter

Log In

1

Legal Data Science Jobs (NOW HIRING)

Citigroup Inc.

VP Marketing Data Science

Manhattan, NY · On-site

Overview The VP, Marketing Data Science is a highly experienced and senior role, part of the larger ... Extensive experience working closely with legal, compliance and procurement teams on technology ...

The Chemours Company

AI & Data Science Intern

Gather UX requirements, and build a front-end/UI (using React, Streamlit, etc.) for the Legal team ... Doctoral degree in Data Science, Computer Science, AI, Software Engineering, Computational ...

The Chemours Company

AI & Data Science Intern

Gather UX requirements, and build a front-end/UI (using React, Streamlit, etc.) for the Legal team ... Doctoral degree in Data Science, Computer Science, AI, Software Engineering, Computational ...

previous page

Showing results 41-60

All JobsLegal JobsLaw Office And Legal Clinic JobsLegal Services Professional JobsLegal Data Science Jobs

Legal Data Science information

See salary details

$27.5K

$53.3K

$85K

How much do legal data science jobs pay per year?

As of Sep 13, 2026, the average yearly pay for legal data science in the United States is $53,278.00, according to ZipRecruiter salary data. Most workers in this role earn between $44,000.00 and $60,000.00 per year, depending on experience, location, and employer.

What is legal data science?

Legal data science is the application of data analysis, statistical methods, and machine learning to legal data and processes. It involves extracting, processing, and interpreting large volumes of legal documents, such as court decisions, contracts, or case filings, to uncover trends, predict outcomes, automate tasks, and support decision-making in the legal industry. Legal data scientists often work with law firms, corporations, or government agencies to improve efficiency and gain insights from complex legal datasets.

What are some common challenges faced by professionals in legal data science roles?

Professionals in Legal Data Science often face challenges related to data quality and accessibility, as legal data is typically unstructured, sensitive, and dispersed across multiple sources. Navigating privacy regulations, ensuring data security, and maintaining confidentiality are critical aspects of the job. Additionally, legal data scientists must bridge the gap between legal teams and technical stakeholders, translating complex legal requirements into actionable data solutions. Effective collaboration and strong communication skills are essential to address these challenges and drive successful outcomes.

What are the key skills and qualifications needed to thrive as a legal data scientist, and why are they important?

To thrive as a Legal Data Scientist, you need a strong background in data analysis, statistics, and legal principles, often supported by degrees in law, computer science, or related fields. Familiarity with programming languages like Python or R, machine learning frameworks, and legal research databases is typically required. Excellent analytical thinking, attention to detail, and effective communication skills are crucial for interpreting complex legal data and collaborating with legal professionals. These skills enable you to extract valuable insights from legal datasets, drive data-informed decisions, and support compliance and litigation strategies.

What is the difference between Legal Data Science vs Legal Analyst?

AspectLegal Data ScienceLegal Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; knowledge of legal conceptsLegal degree or paralegal certification; understanding of legal procedures
Work EnvironmentData-driven teams, tech-focused settings, law firms, or legal departmentsLaw firms, corporate legal departments, courts
Employer & Industry UsageLegal tech companies, law firms, corporate legal teamsLaw firms, government agencies, corporate legal departments
Common Search & ComparisonLegal Data Science vs Legal Analyst

Legal Data Science focuses on analyzing large legal datasets using data science techniques, while Legal Analysts interpret legal information and support casework. Both roles are essential in legal settings but differ in technical skills and focus areas.

More about Legal Data Science jobs

What cities are hiring for Legal Data Science jobs?

Cities with the most Legal Data Science job openings:

What are popular job titles for Legal Data Science?

Popular job titles for Legal Data Science:

Legal Data Science jobs near you
Infographic showing various Legal Data Science job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 80% In-person, and 20% Hybrid job distribution, with an average salary of $53,278 per year, or $25.6 per hour.

Director, Product Management - Data Intelligence Foundation

Denver, CO • On-site

Relativity
See more jobs at Relativityexternal

Other

Re-posted 5 days ago

Key responsibilities

  • Own the full product management layer across multiple engineering organizations for the Data Intelligence Foundation, including defining and managing data primitives such as Files, Ontology, Data Capabilities, Knowledge/Metadata, and Query Plane.

  • Drive the development and evolution of the foundational data platform to ensure it supports AI applications, legal data reasoning, and reliable retrieval, with a focus on performance, consistency, and accessibility.

  • Establish and lead organizational cadence, planning, and decision-making processes to ensure alignment, velocity, and effective trade-off resolution across the product pillars.

Job description

Posting Type

Remote/Hybrid

Job Overview

Relativity is a leading legal data intelligence company building AI technology that helps organizations organize data, discover the truth, and act on it with confidence. Over two decades, the company has built the most trusted platform in legal data, earning deep relationships with the world's leading law firms, corporations, and government agencies, and managing petabytes of the most sensitive data in existence. That foundation is now being turned into something larger: The AI platform for legal data intelligence.
The Relativity Intelligence Model is the architecture for that transformation. At its base is the Foundational Layer (Relativity's shared data platform serving all AI applications). Five primitives give AI agents the structure, meaning, and retrieval capability they need to reason over legal data at scale: Files, Ontology, Data Capabilities, Knowledge/Metadata, and Query Plane. Relativity's Data Intelligence Foundation engineering org is building this layer. The product leadership that shapes it, drives adoption across Relativity's product teams, and builds the PM discipline to own it long-term. That's this role.
The Director of Product Management, Data Intelligence Foundation is one of the highest-leverage product roles at Relativity. The Foundational Layer is what makes every Relativity aiR application smarter, every agent more reliable, and every Relativity product team faster. Getting it right matters enormously.

Job Description and Requirements

Whatyou'llown

The full PM layer across multiple engineering orgs:

  • Files / Natives: The storage primitive for legal documents, images, and native files. You define the substrate that makes immutable legal data consistently accessible across every product, partner integration, and AI workflow, with the SLAs, access contracts, and API surface that teams canbuild onwith confidence. The underlying data primitives are the foundation; the degree to which the retrieval layer (Query Plane) matches this structuredetermineshow easily the organization can navigate between "slow data" and "fast data" use cases.

  • Ontology / Relationship: The semantic layer of the Relativity Intelligence Model. Ontology encodesmeaning:what kinds of things exist in legal data and how they relate, so that AI agents can reason, not just query. You define what Relativity's Ontology becomes: the entities, relationships, and contracts that give every Skill and Agent a shared vocabulary for legal data.

  • Data Capabilities: Reporting, Audit, and internal data infrastructure. The operational backbone that makes the platform observable, auditable, and explainable. These are non-negotiable properties in legal data intelligence use cases.

  • Knowledge / Metadata: The core data model that every product team, customer, and integration partner works with. A unified materialized document layer, consistent across all workspaces, is the mandate. Your roadmap evolves this surface to serve AI application teams as first-class consumers alongside the users who have relied on it for years.

  • Query Plane: One of the most performance-sensitive and strategically important services in the product. The mandate is a unified retrieval pillar with a rich materialized document layer: standardized ingestion APIs independent of data source, hybrid retrieval (lexical + vector) with reranking, chunking as a managed capability, and tiered storage (cold/warm/hot).

Minimum qualifications

  • 12+ years in product management; 5+ yearsleading platform or infrastructure PM organizations

  • Deep fluency with data platform primitives, including storage systems, metadata layers, knowledge graphs, query engines, or equivalent. You can design an API contract, contribute to engineering scope decisions, and articulate the trade-offs in a data consistency model.

  • Demonstrated ability to manage and build a PM team through hiring, leveling, and establishing product practice for a new domain

  • Bachelor's degree in Business, Computer Science, Engineering, or Design, or comparable work experience

Preferred qualifications

  • Experience building at companies where the data platform is the product, not a supporting system.Snowflake, Databricks, Elastic, MongoDB, Palantir, and similar are strong indicators of the right background.

  • Experience building for AI systems, agents, or ML pipelines as primary consumers. You understand what a model needs from data that a humandoesn't, and you design for both.

Ways of working

  • Engineering credibility is paramount.You will be in technical discussions with skilled and knowledgeable engineering leaders regularly.You need to be a peer in those conversations, not a relay.

  • Organizational cadence.Youestablishthe operating rhythm for a multi-pillar PM org: the forum structure, planning cadence, and decision frameworks that let the team move with velocity and alignment. When priorities conflict across pillars, you hold the trade-off clearly and resolve it cleanly.

  • Internal GTM ownership.Adoption of the Foundational Layer means every product team at Relativity builds with it. You define how internal teams discover, integrate, and get value from platform services, and you measure it. This is a product strategy job and a change management job simultaneously.

  • Coaching a scaling PM org.You build PM capability, not just PM headcount, leveling people up while running at speed.

  • Cross-functional influence without authority.Youdon'tcontrol the teams that need to adopt what you build. You make the new path clearly better and bring teams along through clarity, evidence, and trust.

  • Directional clarity under ambiguity.Several of these primitives are being defined as the engineering teams build. You make good decisions with incomplete information and update them whenrequired.

  • Legal domain expertise is notrequired.However, internalizing why defensibility, chain of custody, and auditability are first-class design requirements.

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

$188,000 and $282,000

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.

Required Skills:

Apply