Manager

• Define and lead the MCX customer data strategy and roadmap that supports personalization, predictive analytics, and AI/ML enablement for Marketing and CX functions.
• Architect scalable data infrastructure and pipelines in strategic partnership with data engineering and platform teams, enabling unified customer views and real-time analytics.
• Own and deliver the unified customer view across platforms, developing and managing a 360 degree of the customer to drive intelligent targeting and interactions (CRM, marketing automation, service, commerce).
• Lead development, deployment, and optimization of predictive models and AI/ML solutions for segmentation, churn, personalization, and next-best-action.
• Evaluate, prioritize, and manage AI/ML use cases across MCX, defining, developing and managing the AI/ML roadmap for the Marketing/CX function.
• Single point of contact for translating MCX business needs into modeling requirements and ensure technical feasibility and scalability, working with data scientists and commercial analytics to develop, deploy and enhance.
• Proactively identify and pursue opportunities to solve difficult and high-value problems using data, analytics, and AI/ML.
• Guide and consult broad set of stakeholders on complex data science concepts, models, and findings into clear, compelling narratives tailored for executive audiences.
• Accountable for business impact of advanced analytics including developing visualizations and presentations that highlight business impact, strategic relevance, and actionable insights.
• Influence senior stakeholders by connecting technical outcomes to business priorities, enabling informed decision-making and investment alignment.
• Activate product managers, CX and marketing teams, embedding models into campaigns, workflows, and customer experiences.
• Drive operationalization of models across platforms (e.g., Salesforce, marketing automation, orchestration tools), ensuring performance, scalability, and ROI.
• Lead pilot initiatives and long-term rollouts of model-based capabilities across CRM, analytics platforms, and downstream systems.
• Ensure models are not only technically sound but operationally scalable, integrated into business workflows, and optimized for real-world performance.
• Lead cross-functional teams to embed predictive capabilities into customer-facing and internal systems, driving measurable value and continuous improvement.
• Establish governance frameworks for ethical AI use, model lifecycle management, and data compliance.

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