Data Scientist II - Customer Analytics

About the Role

What You'll Do

• 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.
   

Who You Are

Required Qualifications

• 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.

Preferred Qualifications

• 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.