Senior Manager – Analytics

ROLE RESPONSIBILITIES.Portfolio Strategy & prioritization: Own the analytics roadmap aligned to P&L goals. Prioritize a balanced portfolio of insight, automation, and ML initiatives with clear value cases and success metricsOperating Model & Delivery Excellence: Lead agile planning and execution across analysts, data scientists, BI and engineering. Remove blockers, manage scope/risks, and ensure on-time, production-grade delivery.Adoption, Change Management & Value Realization: Drive stakeholder alignment, training, and process changes required to embed analytics into decisions. Track realized benefits (savings, revenue uplift, efficiency) and course correct.Data, Cloud & AI Governance: Set standards for data quality, documentation, MLOps, cost-efficient cloud usage, security, and responsible AI. Establish guardrails and review mechanisms.People & Vendor Leadership: Hire, coach, and develop a high-performing team; set clear growth paths and expectations. Evaluate and manage external partners and tools to accelerate outcomes.Measurement & Executive Communication: Define metrics and KPI frameworks for the function. Communicate progress and impact crisply to senior leadership through concise narratives and dashboards.ROLE REQUIREMENTSMaster's degree preferred (STEM or MBA with Analytics); Bachelor's in STEM acceptable with significant leadership experience in analytics/data science10 to 12+ years of overall work experience and 6 to 8 years relevant experience leading analytics/data science initiatives and teams with proven P&L impact, preferably in multi-country retailDemonstrated track record of leading an analytics/data science portfolio to quantified P&L impact—revenue growth, cost savings, and operational efficiency—through sharp prioritization, change management, and scaled adoption of data-driven solutionsSets analytics/DS strategy and standards; prioritizes high-ROI problems and defines success metrics tied to P&LStrong grounding in SQL/Python and GA4; able to review code/approaches and uphold quality without being purely hands-onFamiliar with Databricks and AWS cost-efficient architectures; governs MLOps, data quality, security, and responsible AI practicesTranslates business goals into roadmaps covering insights, automation, and ML solutions; ensures production-grade delivery and adoptionChampions experimentation culture, measurement frameworks, and documentation across the teamExceptional stakeholder management; turns insights and model outputs into decisions and process change