URBN Lead Data Analyst

URBN•Philadelphia, PA

About The Position

URBN is seeking a Lead Data Analyst to join its Data Science & Analytics team, which partners with URBN brands and shared-service teams to use data to improve decisions. This hands-on technical leadership role will initially focus on Inventory Science, partnering with Supply Chain, Merchandising/Planning, Data Scientists, and ML Engineers to improve inventory and fulfillment decisions. Over time, the role may support analytical initiatives across URBN brands and Technology teams, including marketing, customer analytics, search, and personalization. The role combines deep SQL and analytical expertise with business curiosity, the ability to investigate data beneath dashboards, and the ability to connect analytical findings to business outcomes. It may grow into people leadership as the team expands. The Lead Data Analyst will develop data foundations and analytical enablement, lead complex analyses and business partnerships, and guide technical quality and team practices. The initial inventory focus includes purchase order splitting, retail store allocation, and order sourcing.

Requirements

At least 5 years of experience in analytics or quantitative roles, including ownership of complex analyses that influenced business decisions.
Advanced SQL skills, including complex queries, performance optimization, and data validation.
Proficiency in Python for analysis, including pandas and standard analytical libraries.
Experience developing analytical datasets and data models in a modern data warehouse environment such as Snowflake, BigQuery, or Redshift.
Strong analytical problem-solving skills, including statistical analysis, identifying and mitigating potential biases, and independently navigating ambiguous business questions.
Experience working across source systems and collaborating with Data Engineering to investigate data quality issues.
Ability to provide technical guidance, mentor peers, and promote analytical best practices.
Strong communication skills and ability to translate complex findings for technical and business stakeholders.
Experience using AI-powered analytical or coding tools with appropriate validation and judgment.
Bachelor's degree in a quantitative field or equivalent practical experience.

Nice To Haves

Experience with BI tools such as Qlik or Looker.
Experience with supply chain, inventory, fulfillment, marketing, or customer analytics.
Experience with experimental design, A/B testing, or causal inference.
Experience with version control and collaborative code review.
Familiarity with AI integrations and workflows, including MCP/connectors and agentic tools.
Experience in a multi-brand or omnichannel retail environment.

Responsibilities

Investigate data across source systems, validate accuracy, resolve discrepancies, and trace issues to root causes.
Build analytical datasets and data models for advanced analysis, experimentation, and data science initiatives.
Partner with Data Engineering to define analytical data requirements and address upstream data quality issues.
Develop measurement frameworks and design experiments, holdouts, and other methods to evaluate business initiatives and data science solutions.
Develop dashboards and self-service reporting in Qlik and Looker, and document key metrics, methodologies, and data lineage.
Lead complex analytical initiatives, translating ambiguous business questions into actionable insights and recommendations.
Partner with business and Technology stakeholders to identify opportunities, quantify potential impact, and define success metrics.
Collaborate with Data Scientists, ML Engineers, and business partners to frame problems, establish analytical requirements, and communicate findings.
Translate complex analyses into clear data stories that explain insights, business implications, and actionable recommendations.
Guide analytical approaches as a technical lead and ensure results are high-quality and defensible.
Establish and promote best practices for SQL, code quality, analytical rigor, validation, and documentation.
Mentor analysts through technical guidance, code reviews, and collaborative problem-solving.
Support inventory and fulfillment optimization initiatives, including purchase order splitting, retail store allocation, and order sourcing.
Size and prioritize opportunities, quantifying current-state costs and potential improvements in dollars, units, service levels, and labor.
Define and own performance metrics such as sell-through, fill rate, split shipment rate, and cost-to-serve, and evaluate optimization solutions.
Translate operational and merchandising constraints into analytical requirements and investigate unexpected outcomes to support model development and improvement.