Lead Applied Research Scientist - I
Thomson ReutersRemotePosted 7 April 2026
Tech Stack
Job Description
A n experienced Lead Applied Scientists with a strong track record of delivering impactful ML/NLP work. Can own the science/research aspect of a small project and confidently interact with stakeholders/experts to define scope and deliverables. Can lead a small team of scientists working on the project and define sprints/tasks/milestones. Champions best practices/ways of working. Understands engineering/deployment aspects of ML. Needs to work on wider awareness and impact beyond individual projects and with other roles (engineers/designers).
About the role
Provide granular scientific leadership, owning the research and development of ML/NLP models and pipelines from concept to delivery
Lead small to large, complex projects, including defining scope, sprints, tasks, and milestones for a team of scientists
Act as the technical lead and key point of reference on projects, ensuring high-quality research deliverables
Translate customer and stakeholder needs into clear technical requirements, and work directly with cross-functional partners
Contribute to long-term AI strategy by providing input to business stakeholders, mid–senior leadership, and Labs leadership
Develop deep knowledge of TR customers and data infrastructure across multiple products to inform solution design and shared Labs capabilities
Champion best practices, effective ways of working, and strong collaboration with engineering and design teams
About you
Experienced ML/NLP scientist with a strong track record of delivering impactful research and product outcomes.
10 years of Experience candidates with Strong focus on NLP and Machine Learning
Able to independently own the science/research aspect of projects and confidently engage with stakeholders and subject-matter experts
Demonstrated expertise through product deliverables, published research, and/or intellectual property in one or more relevant technical areas
Proven ability to design novel ML/NLP approaches to complex business problems and contribute to broader research themes and capabilities
Solid understanding of engineering and deployment aspects of ML solutions
Recognized as a main technical point of reference and trusted advisor within project teams
Committed mentor and coach, supporting individuals with varied ML/NLP experience and helping to grow the overall capability of the team.
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What’s in it For You?
Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected.
Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future.
Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
Social Impact: Make an impact in your community wi ... (truncated, view full listing at source)
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