Data Scientist

JCPenney•Dallas, TX

30d•Onsite

About The Position

The Manager, Data Science – Planning & Allocation is responsible for developing and operationalizing analytical models, insights, and decision tools that optimize inventory placement, forecasting, allocation, replenishment, and product flow across the enterprise. This role is embedded within the business, partnering closely with Planning & Allocation (P&A), Merchandising, Merchandise Operations, Supply Chain, Finance, and Technology teams to deliver data‑driven strategies that improve sales, profit, inventory productivity, and customer experience. Success in this role requires strong analytical leadership, business fluency, and the ability to build trust and adoption of AI‑driven decisioning.

Requirements

Bachelor’s or Master’s degree in Data Science, Advanced Analytics, Statistics, Mathematics, Finance or a related field.
7+ years of experience in analytics or data science, preferably within planning, allocation, merchandising, or supply chain.
Proven experience embedding analytics into business decision workflows, not just reporting.
Strong proficiency in Python or R and SQL.
Demonstrated ability to support and influence merchandising strategy and initiative development through data driven insights
Strong communication skills with the ability to explain AI‑driven decisions to non‑technical partners.

Nice To Haves

Hands‑on experience with allocation systems, replenishment logic, sizing or grading strategies.
Familiarity with inventory and planning KPIs (e.g., sell‑through, weeks of supply, service level, margin impact).
Experience working with cloud data platforms and BI tools (e.g., Power BI, Tableau).

Responsibilities

Lead development of analytics and models supporting allocation, replenishment, sizing, assortment optimization and grading.
Translate complex data into clear, explainable insights to drive business strategies
Define and manage business rules, constraints, and guardrails embedded in AI decisioning tools
Analyze sales trends, localization, and lifecycle dynamics to improve buys and in‑season management.
Build tools and dashboards that enable faster, data‑driven decisions.
Partner cross‑functionally (Planning & Allocation, Merchandising, Supply Chain, Finance, Technology) to drive adoption, trust, and measurable business outcomes.
Present insights to senior business leaders with a focus on decision clarity, business impact, and execution.
Continuously refine models, assumptions, and tools as assortments, customer behavior, and strategies evolve.
Collaborate with technology and data teams, while remaining accountable for business outcomes—not IT delivery.