Sr. Analyst, Data Consulting

kate spade new york•New York, NY

3d•$115,000 - $125,000

About The Position

The Senior Analyst, Data Analytics is a global role applying advanced analytics, data science, and AI to support growth across Tapestry’s international properties. The role moves beyond routine reporting to deliver forward-looking, actionable insight, encourage data-based measurement, and use automation and AI agents to accelerate decisions and improve operational efficiency. The analyst partners with business teams around the world—including Merchandising, Marketing, Digital, Supply Chain, Retail, and Finance—to define business questions, identify high-impact opportunities, and turn analysis into practical recommendations that improve revenue, gross margin, and customer experience. The position also supports data integrity, reporting standards, analytics tools and architecture, self-service insight, stakeholder training, and collaboration across global analytics, engineering, data-science, and algorithm teams.

Requirements

At least 3 years of hands-on analytics experience; retail, e-commerce, consumer, or consulting experience is preferred.
Solid understanding of core business metrics, unit economics, and management reporting.
Experience applying AI/ML and agentic solutions to forecasting, personalization, or automated reporting.
Advanced SQL and ETL skills.
Strong data-visualization expertise using Tableau, Power BI, or a comparable tool.
Hands-on experience with BigQuery or a comparable cloud data warehouse, and digital analytics platforms such as Google Analytics.
Proficiency in Python or R for analysis and modeling.
Demonstrated AI experience applying machine learning, LLMs, and/or generative AI to business problems.
Ability to turn business metrics into clear, actionable recommendations and support adoption of data-based measurement.
Strong storytelling, communication, and relationship-building skills with business stakeholders.
Track record of analyses that improved revenue, gross margin, or process efficiency.
Ability to prioritize dynamically in a fast-paced, global environment.
Bachelor’s degree in a quantitative or technical discipline.
Natural curiosity and enthusiasm for data analytics.

Nice To Haves

Familiarity with automation and orchestration platforms such as Airflow or Prefect, and with MLOps/LLMOps tooling.
Experience with retail or consumer businesses and global, multi-market operations.
Interest in advancing data culture and promoting data-centric decision-making.
Master’s degree in a quantitative discipline.
Exposure to building AI agents and agentic workflows using LangChain, LangGraph, AutoGen, or comparable tools.

Responsibilities

Support data integrity, reporting standards, tools architecture, and training; partner across functions to develop data-driven analyses, models, and tools that enable self-service insight.
Develop interactive dashboards, dynamic visualizations, and standardized management reporting that reveal leading-indicator insights.
Analyze demand and acquisition drivers, media-spend efficiency, and opportunities to improve revenue and margin; communicate findings through clear, data-driven stories for senior business audiences.
Support organization-wide adoption of analytics by tracking outcomes, documenting impact, and promoting data-based planning and decision-making.
Build and maintain predictive models, automated reporting, and AI-driven recommendations, including for demand forecasting, allocation optimization, and marketing-spend efficiency, and help embed them in core business processes.
Develop AI agents to automate analytical workflows such as performance commentary, report generation, anomaly detection, and self-service business Q&A.
Execute AI/ML and agentic pilot projects, quantify their value, and help scale successful solutions across the global organization.
Collaborate with data-engineering, data-science, and algorithm teams to improve data sourcing, management, and analytics product solutions.
Coordinate with the Global Data Analytics team on reporting standards, best practices, and cross-regional data collaboration.
Maintain data integrity, develop reusable analytics tools, and provide stakeholder training to support accurate, self-service data.
Track business and operational metrics, measure efficiency, and recommend process improvements to maximize sales and gross margin, improve the consumer journey and conversion, and focus on value-creating levers.