Manager, Data Science

Job Description Thomson Reuters is expanding its Data Science and AI organization and is looking for a Manager to lead a team building intelligent products and automation. You will own the full lifecycle of ML and GenAI solutions, from problem framing to production deployment and operation. The work spans traditional machine learning and modern GenAI systems, including LLM applications, RAG, agents, and multi-agent platforms. You will set technical direction, develop your team, and partner with business stakeholders to deliver measurable impact. Key Responsibilities Production deployment and delivery Own end-to-end delivery of ML and GenAI solutions from prototype to monitored production systems. Establish MLOps and LLMOps practices: CI/CD, versioning, testing, observability, drift detection, and cost monitoring. Define evaluation frameworks for both classical models and LLM/agent systems, covering accuracy, groundedness, safety, and regression testing. Technical platform ownership Own the architecture, roadmap, and operations of the team's AI platform, including model serving, vector stores, retrieval pipelines, and orchestration layers. Build reusable agentic capabilities (tools, skills, workflow templates, evaluation harnesses) for adoption across teams and products. Lead build-versus-buy decisions across models, frameworks, and cloud services. Technical thought leadership Set direction for agentic AI, RAG, and multi-agent orchestration, and define architecture patterns and standards. Evaluate emerging models, frameworks, and protocols (e.g., MCP) and translate them into practical adoption. Champion responsible AI, security, and governance in all solutions. Coaching and mentoring Lead, coach, and grow data scientists and AI engineers through feedback, code and design reviews, and career development. Build a culture of rigor, experimentation, and ownership. Hire, onboard, and retain strong talent. Stakeholder partnership Partner with Sales, Marketing, Product, and Operations to prioritize use cases and design solutions. Apply AI and data science to business problems such as workflow automation, customer retention, cross-sell, segmentation, pricing, and recommendations. Communicate results and trade-offs clearly to audiences from engineers to senior leadership. Required Qualifications 8-10 years of experience in data science and machine learning, with models deployed in production. Experience leading, mentoring, or managing data scientists or ML engineers. Hands-on experience building LLM applications, including prompt engineering, RAG, embeddings, and vector search. Experience designing and shipping agentic systems, including tool use, orchestration, and agent evaluation. Strong foundation in statistics, machine learning, and hypothesis testing. Expert Python and strong SQL skills. Experience with scikit-learn, PyTorch, or TensorFlow. Experience with cloud platforms (preferably AWS), containerization, and…