Description

Shopbop and Zappos are looking for a customer-obsessed Data Scientist to join the Customer Analytics organization. This role will be at the center of how we understand, reach, and serve our customers across every channel, not as a support function, but as a driving force behind our customer strategy.

You will build the models that power personalization across sites, email, push, and paid media. You will design the causal frameworks that prove what's actually working versus what just looks like it is. You will apply machine learning, LLMs, and advanced optimization techniques to move us from intuition-driven decisions to evidence-driven ones at scale, across Shopbop and Zappos.

The right candidate combines deep technical skills in machine learning and causal inference with genuine curiosity about customer behavior and retail dynamics. They thrive in ambiguity, move fluidly between model development and business strategy, and communicate complex findings clearly to both technical and non-technical audiences. They should have a collaborative mindset that enables them to work effectively across Lifecycle Marketing, Merchandising, Product, Engineering, and other cross-functional partners. This position sits within the Customer Experience organization.

Key job responsibilities
Design, build, and iterate on customer segmentation models that drive product recommendations, content ranking, intent detection, and customer-specific experiences on site, in email, and in push notifications across Shopbop and Zappos.

Apply advanced optimization techniques — including uplift modeling, to improve real-time decisioning across marketing, digital, and channel experiences.

Apply causal inference methods grounded in econometric and machine learning frameworks, including EconML, DoWhy, and CausalML, to estimate the true incremental lift of personalization strategies and marketing interventions through techniques such as double machine learning, meta-learners (T-learner, S-learner, X-learner), and targeted maximum likelihood estimation.

Build and maintain predictive models for customer preferences and individualized treatment effect models that inform business strategy and investment decisions.

Collaborate with Engineering to build scalable data pipelines, feature stores, and real-time serving infrastructure that support ongoing model development and experimentation.

Partner with engineering teams to deploy data science models and solutions into production across email, site, and paid media channels, ensuring models translate from development into customer-facing impact.

Translate complex analytical and modeling results into clear, actionable recommendations for leadership and cross-functional stakeholders, influencing strategy through evidence rather than intuition.