Data Science Manager, GRS AI Transformation

We are seeking a Data Science Manager to join the Global AI Products and Transformation team within GRS Data Science Solutions. This leader will develop and manage a team of data scientists responsible for scaling ML/AI solutions and creating reusable capabilities that accelerate AI adoption across multiple GRS lines of business and regions. 

The ideal candidate will combine data science expertise, hands-on engineering experience, and collaborative leadership. They will lead a team in delivering production-grade AI solutions and establishing reusable technical patterns and platforms. They will also translate business opportunities into product roadmaps and partner with engineering and architecture teams to define enterprise-wide infrastructure that provides AI systems with governed access to relevant context. 

• Hands-on experience developing and deploying production-grade AI or ML solutions. 
• Advanced knowledge of AI/ML methods, engineering practices, tools, and business applications. 
• Strong communication, collaboration, and stakeholder-management skills. 
• Relevant insurance or regulated-industry experience is preferred. 
• Competencies typically acquired through a Ph.D. degree (in Computer Science,  

Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of  6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of  8 years of relevant experience. 

• Demonstrated leadership experience, including managing, coaching, or mentoring data scientists, engineers, interns, or external partners. Prior formal people-management experience is preferred. 
• Experience leading AI products in a product-oriented delivery model. 
• Experience with GenAI, LLM-based applications, Q&A, chat, embeddings, prompt engineering, retrieval-augmented generation, or AI-assisted workflows. 
• Experience building reusable AI platforms, shared services, or enterprise-scale capabilities. 
• Success driving AI transformation and adoption in large, complex, or global organizations, if applicable. 

• Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Expert knowledge of predictive toolset; reflects as expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or SBU.
• Ability to give effective training and presentations to peers, management and less senior business leaders.
• Ability to use results of analysis to persuade team or department management to a particular course of action.
• Has a value driven perspective with regard to understanding of work context and impact.
• Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 2 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelor`s degree(scientific field of study) and a minimum of 5+ years of relevant experience.

• →Lead, coach, and develop a high-performing team of data scientists and AI/ML engineers. 
• →Drive the design, development, deployment, and ongoing improvement of scalable AI solutions. 
• →Identify common requirements and solution patterns across use cases, and lead the development of reusable AI products, platform capabilities, shared components, frameworks, APIs, and patterns that enable downstream applications. 
• →Partner with AI product managers, engineers, architects, and business stakeholders to design enterprise architecture solutions that enable scalable AI adoption. 
• →Promote strong engineering practices across the AI development lifecycle, including testing, version control, CI/CD, observability, documentation, and MLOps/AIOps. 
• →Communicate technical concepts, trade-offs, progress, and recommendations clearly to both technical and nontechnical audiences. 

• Success in this role means accelerating the delivery of AI solutions while reducing duplicated effort across the organization. The Data Science Manager will create the technical and organizational foundation that allows teams to move efficiently from AI experimentation to secure, reliable, and reusable production capabilities.