Lead Applied Machine Learning Scientist
Thomson ReutersRemotePosted 7 April 2026
Tech Stack
Job Description
re you excited about working at the forefront of applied research in an industry setting? Thomson Reuters Labs
in São Paulo is seeking scientists with a passion for solving problems using state-of-the-art information retrieval, natural language processing and generative AI.
What does Thomson Reuters Labs do? We experiment, we build, we deliver. We support the organization and our customers through applied research in informational retrieval and natural language processing. We work closely with product and domain experts to identify compelling solutions at the intersection of user need and technical feasibility. Our team is designing the next generation of search technology for Legal and Tax Professionals globally. We drive AI innovation for Thomson Reuters’
About the Role
Lead Applied Scientists are experts in Machine Learning / NLP, responsible for the design and delivery of AI solutions that enhance Thomson Reuters' products. They leverage information retrieval techniques, prompting workflows, model training and evaluation design to build and optimize solutions. Their work ensures AI technologies are effectively aligned with business objectives, driving product innovation and value.
As a Lead Applied Scientist, you will:
Innovate and drive solution delivery as a technical leader
Be fully accountable for all research deliverables
Provide input to the business and Labs leadership on long term AI strategy.
Lead and drive stakeholder engagement with other functions (UX, Product, Tech)
Develop in-depth knowledge of customer problems and data
Maintain scientific and technical expertise in one or more relevant areas as demonstrated through product deliverables, published research, and intellectual property.
Mentor and coach other scientists and engineers on best practices
About You
You’re a fit for the role of Lead Applied Scientist if your background includes:
Master in a relevant discipline or Master’s plus a comparable level of experience
Hands-on experience building IR / NLP systems for commercial applications
Experience writing production code and ensuring well-managed software delivery
Advanced English
Demonstrable experience translating complex problems into successful AI applications
Professional experience scaling yourself and leading through others, in an applied research setting
Outstanding communication, problem solving, and analysis skills
Collaborating with Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback
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O que iremos te oferecer?
Modelo de Trabalho Híbrido: Adotamos um ambiente de trabalho híbrido e flexível (2-3 dias por semana no escritório, dependendo do papel) para funções baseadas em escritório, garantindo uma experiência integrada e coerente tanto no âmbito digital quanto presencial.
Flexibilidade e Equilíbrio entre Trabalho e Vida Pessoal: Flex My Way consiste em um conjunto de políticas de projetadas para facilitar a gestão das responsabilidades pessoais e profissionais, seja cuidando da família, participando de atividades comunitárias ou dedicando tempo para descanso e renovação pessoal. Essas políticas se baseiam em nossa abordagem flexível de trabalho, permitindo que os funcionários trabalhem de qualquer lugar por até 8 semanas por ano, promovendo assim um equilíbrio ideal entre seus compromissos de trabalho e pessoais.
Desenvolvimento e Crescimento Profissional: Promovemos uma cultura de aprendizado contínuo e desenvolvimento de habilidades, preparando nossos talentos para enfrentar os desafios do futuro e oferecer soluções reais. Nosso programa Grow My Way e a abordagem focada em habilidades garantem que você tenha as ferramentas e conhecimentos para crescer, liderar e prosperar em um futuro habilitado pela inteligência artificial.
Benefícios Competitivos do Setor: Oferecemos planos de benefícios abrangentes que incluem assistência médica e odontológica, dois dias de feriado corporativo para sa ... (truncated, view full listing at source)
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