2mo ago

Data Scientist II - Customer Analytics

AnalyticsData & AI

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Quick Summary

Overview

About the RoleThe Data Science team at Gap Inc. transforms customer, product, digital, and market data into insights that drive measurable business impact.

Technical Tools

AnalyticsData & AI

About the Role

~1 min read

The Data Science team at Gap Inc. transforms customer, product, digital, and market data into insights that drive measurable business impact. The successful candidate will lead high-impact analyses across customer acquisition and retention, product and basket performance, digital engagement, and omnichannel behavior.

This role requires analytical rigor, business acumen, data storytelling, and strong stakeholder management. The ideal candidate can structure ambiguous business questions, connect insights across internal and external data sources, and clearly articulate what is happening, why it matters, and what actions to take.

Responsibilities

~1 min read

→The ideal candidate is an applied, business-facing data scientist who pairs strong technical capabilities with a passion for working directly with business partners, using analytics and modeling to drive decisions rather than focusing primarily on behind-the-scenes model development.
→Lead end-to-end analytical projects, from framing complex business questions and developing the analytical approach to delivering actionable recommendations and measuring impact.
→Analyze large customer, transaction, product, marketing, and digital datasets to identify growth opportunities and business performance drivers.
→Translate complex findings into concise, executive-ready narratives that clearly communicate the “so what,” recommended actions, trade-offs, and expected impact.
→Develop customer and product analytics—including lifecycle, segmentation, basket, cross-shopping, cohort, funnel, and behavioral analyses—to inform business strategy.
→Integrate internal data with external sources, such as consumer trends, search behavior, competitive intelligence, and market research, to build a holistic view of the customer and business.
→Apply statistical, predictive, causal, and machine learning techniques when appropriate to improve business decision-making.
→Develop scalable analytical frameworks, reusable datasets, and automated processes that enable consistent measurement and reduce manual work.
→Partner with business leaders, product managers, data scientists, engineers, and technology teams to translate business priorities into analytical roadmaps and deliverables.
→Build trusted stakeholder relationships, proactively identify emerging needs, constructively challenge assumptions, and influence decisions through clear, data-backed recommendations.

Requirements

~1 min read

Bachelor’s degree with 5+ years of relevant experience, or master’s degree with 3+ years of relevant experience, in Data Science, Statistics, Economics, Computer Science, Applied Mathematics, or another quantitative field.
Proven ability to lead complex analytical projects from problem definition through recommendation and business activation.
Advanced proficiency in SQL and experience working with large datasets in distributed or cloud environments.
Proficiency in Python, R, Spark, or other modern analytics and data science tools.
Strong written and verbal communication skills, including experience presenting recommendations to senior stakeholders.
Self-starter with the ability to work independently, proactively drive work forward, manage multiple priorities, and collaborate effectively across functions.

Experience in DTC retail, apparel, e-commerce, consumer products, or another customer-focused industry.
Experience analyzing customer lifecycle, acquisition, retention, loyalty, product performance, or omnichannel behavior.
Experience with basket analysis, product affinity, cross-shopping, or customer-product relationships.
Experience integrating external data—such as search trends, consumer research, competitive intelligence, social listening, or market data—with internal performance data.
Experience with digital analytics, including website or app engagement, customer journeys, conversion funnels, and marketing channel performance.