Director, Demand Optimization

Director, Demand Optimization - Stamford, CT Be a part of a revolutionary change! At Philip Morris International (PMI), we’ve chosen to do something incredible. We’re totally transforming our business and building our future on one clear purpose – to deliver a smoke‑free future. With huge change comes huge opportunity. So, if you join us, you’ll enjoy freedom to dream up and deliver better, brighter solutions and the space to move your career forward in endlessly different directions. Our success depends on people who are committed to our purpose and have an appetite for progress. Our beautiful HQ in Stamford, CT is just steps away from the Stamford Metro‑North Train Station and easily accessible from NYC. Position Overview The Director of the Demand Realization Engine leads the data infrastructure, analytics, and commercial measurement capabilities that optimize go‑to‑market execution. This role owns the ‘how’—building the data ecosystems, analytical models, and performance measurement frameworks that turn strategy into results. The Director architects the integration of retail data, consumer behavior data, and marketing effectiveness analytics into a unified system that drives competitive advantage for ZYN and IQOS. Your ‘day To Day’ Lead the Demand Realization Engine, including Retail Data Ecosystem, Consumer Data Ecosystem, and Measurement & Optimization teams, ensuring integrated operation across capabilities. Develop and coach a high‑performing team of Senior Managers and Managers, building next‑generation analytics and data talent with strong technical and business capabilities. Build the ‘one version of truth’ for performance measurement, aligning stakeholders around consistent metrics and methodologies. Architect the data infrastructure that integrates retail data (POS, syndicated, retailer platforms), consumer behavioral data (panels, loyalty, digital signals), and marketing effectiveness data into a unified intelligence system. Partner with IT, business intelligence, and commercial teams Advanced Analytics and RGM teams to enable scalable, automated data platforms and reporting capabilities that serve the organization efficiently. Own marketing effectiveness measurement, including marketing mix modeling (MMM), multi‑touch attribution (MTA), promotional ROI analysis, and incrementality testing. Establish data governance, quality standards, and documentation practices across all data assets to ensure reliability and trust. Drive innovation in analytics capabilities, including AI/ML applications, predictive modeling, automated insight generation, and real‑time decisioning where appropriate. Partner with the Demand Creation Engine to ensure analytics capabilities support strategic consumer insights and measurement needs. Manage vendor relationships for data platforms, analytics tools, technology partners, and data providers, ensuring maximum value from investments. Translate technical capabilities into business value, building understanding and adoption across commercial stakeholders. Key Competencies And Skills Data Architecture Leadership: Deep expertise in designing data infrastructure that integrates diverse sources, ensures quality, and scales with the business. Understanding of modern data architecture patterns and technologies. Analytics Methodology Expertise: Strong technical fluency in analytics methodologies—marketing mix modeling, attribution, predictive modeling, segmentation—with ability to evaluate approaches and guide application. Technical‑Business Translation: Ability to translate technical capabilities into business value and communicate with both technical teams and business stakeholders. Making analytics accessible and actionable. Team Leadership: Building and developing high‑performing analytics teams with strong technical skills and business acumen. Creating career paths and development opportunities. IT Partnership: Ability to partner effectively with IT, data engineering, and technology teams. Understanding of enterprise technology considerations and constraints. Innovation Orientation: Staying current with emerging analytics technologies (AI/ML, automation) and knowing when and how to apply them. Balancing innovation with pragmatic execution. Stakeholder Management: Building alignment across commercial stakeholders with different needs and perspectives. Establishing credibility as the authority on data and measurement. Vendor Management: Evaluating and managing technology vendors, data providers, and analytics partners. Maximizing value from the technology and data portfolio. Who We’re Looking For Bachelor’s degree required. 12+ years of progressive experience in data analytics, data architectur