Decision Data Scientist

Candidates must be local to NYC.

Decision Data Scientist will work part of a small team and help build, operationalize, and own predictive models that improve decision making across Planning, Merchandising/Buying, Customer 360, and Inventory Management.

This person will work primarily with a core analytics and deployment platform and bridge data science and platform engineering, translating real-world commercial constraints including seasonality, product lifecycle, allocations, replenishment, promotions/markdowns, and customer behavior into practical, production-grade, human-in-the-loop models. This is a highly cross-functional role with direct, ongoing exposure to business stakeholders and end users, from planners and buyers who consume and act on model outputs, to the teams working within their data platform application day-to-day.

Responsibilities

• Design, build, and maintain predictive and statistical models supporting retail planning, buying, inventory, and demand forecasting.
• Own and evolve analytics logic and assumptions embedded in planning and buying workflows.
• Partner with Planning, Buying, Merchandising, and DTC teams to translate business needs into model-driven insights, maintaining direct, ongoing relationships with the stakeholders and end users consuming model outputs.
• Collaborate with the Enterprise Applications team on Workshop app development to ensure model outputs are surfaced effectively in business-facing data platform applications.
• Build models for customer segmentation, CLV, retention/churn, propensity, and campaign targeting (Customer 360).
• Collaborate with Engineering, who owns data pipelines and Snowflake ingestion, to ensure model inputs are reliable, well-governed, and fit for purpose without owning the underlying data infrastructure.

Qualifications

• 4 to 7 years of experience in data science, applied analytics, or predictive modeling.
• Strong experience building predictive or statistical models including forecasting, optimization, regression, and classification.
• Proficiency in Python for data analysis, modeling, and production-grade code.
• Strong SQL and hands-on experience with Snowflake or equivalent cloud data warehouse.
• Experience with time-series forecasting at scale.
• Retail domain experience or DTC industry experience is required, including familiarity with demand vs net sales, sell-through, markdowns, planning vs buying workflows, seasons, price groups, and store vs e-commerce dynamics.