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Quince is a manufacturer-to-consumer (M2C) retailer delivering high-quality essentials at low prices through direct relationships with manufacturing partners. The company spans apparel, accessories, home goods, and more, operating across multiple countries with tens of millions of customers.
Role Overview
You will architect and own the forecasting and planning workflows for Quince's international expansion. This is a builder role where you design how forecasting logic is structured, evolves, and advances—particularly for international market replenishment. You'll develop the logic and continuously refine the replenishment workflow for international business, integrating with existing planning systems and ordering workflows. This workflow will synthesize market fit data, seasonality, and inventory position to generate buys for international markets using a blend of machine learning, classical forecasting, and AI agents.
You'll prototype and ship early versions yourself when speed matters, and scope work properly with data and engineering partners when scale is required. You'll be the technical partner to a Senior Inventory Planner who operates the system day-to-day, making their decisions smarter, faster, and more defensible. Your goal is to build planning intelligence that lets Quince enter every new market faster than the last.
Key Responsibilities
- Define the forecasting and planning method for international expansion: which models live where, what AI capabilities sit on top, what humans own, and how pieces fit together end-to-end.
- Build and ship early versions yourself: prototypes in Python, SQL pipelines, Streamlit tools, LLM-powered workflows, and agent prototypes. Deliver quick wins in weeks, not quarters.
- Partner deeply with data and BI teams to harden, scale, and productionize what works.
- Work alongside PlanIt and existing planning workflows to build on top where it makes sense and bridge gaps where it doesn't.
- Develop and own a point of view on AI in planning: where LLMs and agents add value, where classical models still win, and how Quince stays ahead.
- Partner directly with the Senior Inventory Planner, surfacing what the model is saying, why, and where it's unsure. Feed their feedback back into the architecture.
- Build analytical foundations for leadership: forecast accuracy reporting, scenario modeling, and ad hoc deep-dives that change decision-making.
- Codify planning processes that don't exist yet, translating them into systems or well-designed manual workflows as appropriate.
- Continuously raise the bar on forecast accuracy, planning automation, and day-to-day AI integration.
Success Metrics
- Forecast accuracy improves measurably across launch and replenishment use cases.
- Out-of-stock clicks are minimized.
- AI and ML are genuinely woven into international planning workflows where they earn their place, with a clear, trusted line between "the model says" and "the planner decides."
- International/new market launch planning gets faster, smarter, and more automated every quarter.
- Planners trust the system, challenge it when wrong, and lean on it when right.
- The architecture scales: UK and EU today, but next market launches are faster, smarter, and cheaper.
- Quince's planning function is recognizably ahead of comparable retailers on AI-driven planning.
Requirements
- 5+ years working in or directly adjacent to a planning, forecasting, or supply chain analytics function, ideally at a DTC, e-commerce, or multi-category retail business.
- Strong domain instinct: you've owned forecasts, sat with planners, lived inventory decisions, and know which planning problems are worth solving.
- Genuine working coding ability: Python fluency, comfortable in SQL, uses AI tools (Claude, Cursor, Copilot) as a daily co-pilot. You don't need to write production code, but you need to build the first working version of almost anything.
- A real, articulable point of view on AI in planning: you've experimented with LLMs and agents in your own workflow, know what they're good for and what they break on, and can hold your own in architectural conversations.
- Working knowledge of forecasting techniques across the spectrum: classical statistical models, ML approaches, and where each earns its place.
- Comfort partnering with data and engineering teams: you write requirements they respect, scope work cleanly, and translate between technical and operational language.
- Excellent communicator: you can explain a model's limits to non-technical leaders, brief engineers with enough detail they want to work with you, and tell planners why the system is recommending something counterintuitive.
Preferred
- Experience at a high-growth DTC, e-commerce, or marketplace where planning is a real competitive advantage.
- Hands-on experience with planning platforms (Anaplan, RELEX, o9, ToolsGroup, Streamline, or proprietary equivalents).
- Background in or proximity to launching products or categories with limited history.
- Familiarity with NetSuite or comparable ERP systems.
- Experience shipping LLM-powered or agentic workflows in production or near-production context.
- Track record of cross-functional partnership with data engineering, ML, or platform teams.
- Background in operations research, applied statistics, or formal forecasting training.
