Lead Data Discovery And Enrichment (hybrid Or Remote)
LexisNexis
United Kingdom
Not specified; not specified; generous holiday all...
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Qualified solicitor or barrister
Advanced legal research and writing
Managing coaching and mentoring others
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LexisNexis is seeking a Lead Data Discovery and Enrichment professional to manage a team of experts in the legal domain, focusing on AI projects that enhance legal technology. The role is suitable for qualified solicitors or barristers with advanced legal experience and an understanding of machine learning technologies.
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Job Summary
This role involves managing a team of Data Discovery and Enrichment Experts while defining scope and leading execution of AI projects.
The position requires advanced legal domain expertise to drive insights and recommendations critical to creating the best features and products for customers.
LexisNexis offers a healthy work/life balance with various flexible and remote working options available to employees.
Matching Summary
Match Score: 75
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LexisNexis is seeking a Lead Data Discovery and Enrichment professional to manage a team of experts in the legal domain, focusing on AI projects that enhance legal technology. The role is suitable for qualified solicitors or barristers with advanced legal experience and an understanding of machine learning technologies.
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Salary
Not specified; Not specified; Generous holiday allowance; Health screening; Private medical benefits; Pension scheme; Share option scheme
Skills & Requirements
Must-have
Qualified solicitor or barrister
Advanced legal research and writing
Managing coaching and mentoring others
Intermediate understanding of machine learning
Experience with Excel and PowerPoint
Nice-to-have
Customer-centric mindset
Ability to lead highly complex projects
Advocating for Responsible AI
Flexible remote working options
Collaboration with data scientists
Key Requirements
Must be a qualified solicitor or barrister
Advanced professional legal experience required
Professional experience in managing and mentoring teams