Senior AI Architect

Roles and Responsibilities :

What you'll get to do:

Enterprise AI & Data Science Strategy

• Define and own the multi-year AI and data science strategy aligned to enterprise growth priorities, presenting roadmaps and business cases directly to the C-suite and board.
• Establish the organization’s AI vision—identifying where machine learning, generative AI, and advanced analytics create the highest-leverage impact across the value chain.
• Lead the evaluation and adoption of emerging AI capabilities (LLMs, agentic AI, computer vision, NLP) and determine fit-for-purpose applications across business domains.
• Drive the build-vs-buy strategy for AI/ML platforms and tools, partnering with engineering leadership to shape the technology ecosystem.

Operational Leadership & Execution

• Translate strategic priorities into an actionable portfolio of AI/data science initiatives with clear business KPIs, timelines, and resource plans.
• Own the end-to-end lifecycle of AI/ML solutions: ideation, experimentation, development, productionization, monitoring, and continuous improvement.
• Establish scalable MLOps practices including model versioning, automated retraining pipelines, drift detection, and governance frameworks.
• Create and maintain an AI use case registry that tracks initiative status, business value delivered, and prioritization across all domains.

Consumer Intelligence & Personalization

• Architect the enterprise consumer intelligence strategy—unifying customer data signals to build a holistic understanding of consumer behavior, preferences, and lifetime value.
• Drive production-grade AI solutions including:
• Advanced customer segmentation (behavioral, psychographic, ML-based clustering)
• Customer lifetime value modeling and predictive retention frameworks
• Hyper-personalized recommendations across web, app, email, and in-store
• Churn prediction & proactive win-back strategies powered by real-time signals
• Next-best-action engines for lifecycle marketing orchestration
• Multi-touch attribution & media mix modeling to optimize marketing spend
• Partner with Marketing, CRM, and Digital Product to embed AI into consumer-facing experiences and campaign optimization.

Cross-Functional Influence & Business Partnership

• Serve as the senior analytics thought partner to business leaders across Merchandising, Supply Chain, Retail, Wholesale, International, and DTC channels.
• Identify and champion AI-driven opportunities in demand forecasting, inventory optimization, assortment planning, pricing intelligence, and supply chain efficiency.
• Collaborate with Finance and FP&A to integrate predictive models into planning cycles, scenario analysis, and strategic decision frameworks.
• Influence and align with Data Engineering, Data Platform, and Enterprise Architecture teams to ensure infrastructure supports scalable, production-ready AI workloads.
• Represent the AI and data science function in cross-functional steering committees, vendor evaluations, and strategic planning sessions.

Team Building & Organizational Development

• Build, lead, and mentor a high-performing team of data scientists, ML engineers, and AI specialists across consumer, operational, and emerging AI domains.
• Establish a center of excellence for AI and advanced analytics with reusable frameworks, shared playbooks, and governed best practices.
• Create clear career development paths and a talent strategy that attracts and retains top-tier data science talent in a competitive market.
• Foster a culture of intellectual curiosity, rigorous experimentation, and business-outcome orientation across the team.
• Drive knowledge sharing and AI literacy initiatives to elevate data science capabilities across the broader analytics organization.

Measurement, Governance & Ethical AI

• Define and own the impact measurement framework for all AI initiatives—tracking ROI, business value, and operational lift with rigor and transparency.
• Implement robust A/B and multivariate testing programs to validate personalization, targeting, and optimization strategies before scaling.
• Champion responsible AI practices including fairness, explainability, bias detection, and compliance with data privacy regulations (CCPA, GDPR).
• Establish AI governance standards including model documentation, approval workflows, and regular performance audits.