Data Scientist III, Customer Strategy

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.

Basic Qualifications

• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 4+ years of data scientist experience
• Experience with statistical models e.g. multinomial logistic regression
• Bachelor's degree

Preferred Qualifications

• 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
• Experience managing data pipelines
• Experience as a leader and mentor on a data science team
• Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices
• Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences