Director-Data Science

Director – Data Science, Global Servicing Decision Science

About the Team

The Global Servicing Decision Science (GSDS) team is the Decision Science engine for Global Servicing, bringing together advanced analytics, data science, AI/GenAI, and decisioning to transform servicing experiences, empower colleagues, and enable intelligent operations. GSDS serves as a centralized Decision Science Center of Excellence, partnering across Product/Capabilities, Control Management, Strategy, Operations, Technology, and enterprise analytics and data science teams.

GSDS builds and scales servicing intelligence across Global Servicing—from predictive and proactive servicing, personalization, next-best-action and journey orchestration to conversational AI, agent assist, AI-powered coaching, conduct and quality monitoring, and complaint and dispute intelligence. Our mandate is to build durable, production-ready capabilities that continuously improve decisions through prediction, recommendation, optimization, experimentation, and learning.

How will you make an impact in this role?

As Director, you will define and lead the vision, strategy, and execution of Decision Science across Global Servicing, translating complex customer and business challenges into scalable intelligence capabilities that improve customer experience, colleague effectiveness, operational efficiency, membership value, and controls.

This is a highly visible leadership role requiring deep analytical rigor, strong technical and AI expertise, strategic problem-solving, commercial acumen, and enterprise leadership. You will lead a high-performing team of data science professionals and partner across Global Servicing and the enterprise to embed intelligent decisioning into servicing experiences and processes.

You will also help shape the next generation of servicing Decision Science—advancing capabilities such as Agentic AI, transformer-based recommendation systems, and Conversational AI while ensuring solutions are production-ready, measurable, governed, and continuously improving.

 

GSDS Strategy & Business Impact

• Define and lead the Decision Science roadmap across Global Servicing, identifying and prioritizing high-impact opportunities across servicing experiences, membership value and engagement, colleague efficiency, and operational excellence.

• Translate business strategy into clear Decision Science priorities, success measures, and investment choices, connecting technical opportunities to measurable customer and business outcomes.

AI / GenAI & Decision Science Leadership

• Provide technical leadership across AI/ML, GenAI, prediction, recommendation, optimization, experimentation, and learning, setting a high bar for solution design, evaluation, and analytical rigor.

• Drive the evolution of GSDS toward emerging AI paradigms, including Agentic AI and multi-agent workflows, transformer-based recommenders and representation learning, and Conversational AI/LLM-based systems that can reason over context and support increasingly intelligent servicing decisions.

• Maintain sufficient technical depth to challenge architectures, modeling choices, evaluation frameworks, and trade-offs, and to guide teams from experimentation through scalable production deployment.

Scalable Decisioning Products & Production AI

• Oversee development of reusable, production-ready data science products and decisioning systems that leverage customer, behavioral, contextual, and operational data to enable timely and personalized decisions.

• Partner with Technology, platform, and governance teams to deploy and evolve AI solutions with appropriate architecture, observability, controls, model/AI quality, and lifecycle discipline.

Leadership, Talent & Enterprise Influence

• Lead, mentor, and grow a high-performing team of data science professionals, fostering technical excellence, first-principles problem solving, innovation, ownership, and strong commercial judgment.

• Build organizational capability across Applied GenAI, AI engineering and architecture, advanced recommendation and decisioning methods, strategic problem solving, and commercial storytelling.

• Influence senior stakeholders and build strong cross-functional accountability, using clear and compelling narratives to drive prioritization, investment decisions, adoption, and execution of major Decision Science initiatives.

Innovation & Future-Focused Capability Building

• Continuously evaluate advances in AI and Decision Science and translate the most relevant methods into practical, scalable capabilities and reusable best practices for Global Servicing.

• Shape a forward-looking technical roadmap that strengthens GSDS capabilities in Agentic AI, Conversational AI, modern recommendation systems, experimentation, AI engineering, architecture, governance, and continuous optimization.

Minimum Qualifications

• Bachelors degree in a quantitative field (e.g., Engineering, Computer Science, Mathematics, Statistics, Economics).

• Exceptional analytical and conceptual problem-solving ability, with experience structuring and solving complex, ambiguous business challenges using first-principles thinking.

• Proven leadership experience managing and developing high-performing analytics or data science teams in a complex, cross-functional environment.

• Strong technical foundation in modern data science and AI, with the ability to guide solution design, challenge technical approaches, and connect analytical choices to business outcomes.

• Strong ability to influence senior stakeholders through clear, structured, and compelling communication and executive storytelling.

• Experience driving large-scale analytics, Decision Science, or AI initiatives from concept through production, adoption, and ongoing optimization.

• Strong commercial acumen and storytelling, with the ability to connect technical strategy, architecture and investment choices to measurable business value.

Preferred Qualifications

 

• Master’s degree in a quantitative field (e.g., Engineering, Computer Science, Mathematics, Statistics, Economics).

• Technical exposure to or experience driving modern AI capabilities such as Agentic AI/agentic workflows, transformer architectures and transformer-based recommendation systems, Conversational AI, LLMs, and advanced personalization or next-best-action systems.

• Strong understanding of the end-to-end Decision Science and AI lifecycle, including experimentation, model/AI evaluation, decision logic and orchestration, productionization, monitoring, governance, and continuous optimization.

• Track record of building scalable data products, recommendation/decisioning systems, or AI-driven capabilities with measurable customer, revenue, productivity, cost, or control outcomes.

• Experience driving transformation from traditional analytics toward AI-powered, productized Decision Science capabilities and reusable enterprise solutions.