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# Lead Data Scientist

- **Company:** Xcede
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Python (Programming Language), Microsoft Copilot, SQL Databases, Lexis, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Snowflake, Prompt Engineering, Aderant, Databricks
- **Published:** November 12, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/kpcdsd4v28

## About the Role

* 5+ years as a data scientist, including time leading or setting direction for DS work.
 * A track record of taking models from prototype into production and keeping them running.
 * Hands-on experience building and evaluating LLM solutions, including prompt design, RAG and structured evaluation. Exposure to agents, MCP or context engineering is a plus.
 * Strong Python and SQL, plus experience on a cloud data platform (Databricks, Azure or Snowflake).
 * Experience defining success metrics and showing measurable business impact.
 * Excellent stakeholder skills, with the ability to turn workflows into testable AI proposals and explain trade-offs to senior, non-technical audiences.
 * Experience mentoring or managing others, or a clear ambition to move into people leadership.

Nice to have

 * Professional services, legal or legal tech experience.
 * Background in a regulated, data-driven industry such as financial services or insurance.
 * PE-backed or investor-owned environment experience.
 * Familiarity with legal platforms (ShareDo, Clio, Aderant) or legal AI tools (Jylo, Lexis Protégé, Copilot).
 * Awareness of confidentiality, privilege and data-protection considerations when applying AI to client data.

This is a full-time position with a hybrid set-up from their central London office. Unfortunately visa sponsorship and relocation support are not available for this role.

## Description

* Work with lawyers, subject matter experts, business analysts and engineers to find where AI and automation can have the biggest impact in legal workflows.
 * Prototype LLM-based solutions and test them using human-in-the-loop review and LLM-as-judge evaluation, then guide the successful ones into production.
 * Map data across matter, client, document, time-recording and billing systems, and help define a target data model.
 * Set baselines and success metrics, and build before-and-after analysis and dashboards to show impact on productivity, quality, cost and margin.
 * Monitor models and AI-enabled workflows in production for quality degradation and drift.