Lead Applied Scientist: AI for Law & Compliance

Thomson Reuters; Frisco (TX); Hybrid; USD 127,400 - 236,600; Full time

Benefits offered by this job

Hybrid Work Model

Flexible vacation

Mental Health Days

Headspace access

Tuition reimbursement

Volunteer days

Job summary

Thomson Reuters in the United States seeks a Senior Applied Scientist to lead end-to-end applied research projects that drive product innovation in legal intelligence, tax automation, or regulatory compliance. You will translate business challenges into technical solutions, determine feasibility, and deliver scalable AI capabilities.

This role includes mentoring scientists and influencing technical direction within your team, while starting to grow and scale your impact.

Qualifications

• PhD or Master’s degree in Computer Science, ML, or related field.
• 5+ years of hands-on experience building and deploying production IR, NLP, ML, or GenAI systems.
• Experience leading end-to-end AI project delivery in cross-functional environments.
• Outstanding technical judgment and evaluation expertise.
• Excellent communication skills with ability to influence technical direction.
• Experience developing novel methods for complex domains such as tax or law.
• Publications at ACL, EMNLP, NAACL, NeurIPS, ICLR, SIGIR, or KDD.

Responsibilities

• Lead end-to-end applied research projects to drive product innovation.
• Translate business challenges into scalable AI solutions and determine feasibility.
• Mentor scientists and influence technical direction within the team.
• Grow and scale impact across projects and domains.

Skills

NLP

GenAI

Machine Learning

RAG architectures

Agentic frameworks

Tool-using systems

Orchestration patterns

Publications in NLP venues

Education

PhD or Master’s in CS/ML

Job description

Thomson Reuters in the United States seeks a Senior Applied Scientist to lead end-to-end applied research projects that drive product innovation in legal intelligence, tax automation, or regulatory compliance. You will translate business challenges into technical solutions, determine feasibility, and deliver scalable AI capabilities.

This role includes mentoring scientists and influencing technical direction within your team, while starting to grow and scale your impact.