Role Overview

We're looking for someone who has spent years inside a planning function, who taught themselves to code along the way, who has formed real opinions about where AI fits into the planning stack, and who now wants to architect the answer at Quince.

This is a builder role. You'll own the forecasting and planning workflows and logic covering how it's designed, how it evolves, and how it advances. For Quince international expansion, you will develop the logic and continuously finetune the replenishment workflow for international business tying in with the existing planning systems and ordering workflows. This workflow will consider the latest market fit data, seasonality data and inventory position to generate buys for the international market. You'll define how this all comes together utilizing a blend of machine learning, classical forecasting, and AI agents. You'll try, test and ship prototypes yourself when speed matters, and you'll scope work properly with data and engineering partners when scale matters.

You'll also be the technical partner to a Senior Inventory Planner who operates the system day-to-day, sizing launch buys and executing replenishment. Your job is to make their decisions smarter, faster, and more defensible and to build the planning intelligence that lets Quince enter every new market faster than the last.

You've lived inside the planning function long enough to know which problems are worth solving. You've coded enough, in Python and SQL and increasingly with LLMs and agents, to know what's now possible that wasn't possible eighteen months ago. You have a strong point of view on AI in planning. You're energized by building things that work, not by writing papers about things that might.

You don't wait for permission to prototype. You don't over-engineer.You communicate clearly with both planners and engineers, and you can translate between them, which is half the job.

Success in This Role Means

• Forecast accuracy improves measurably across launch and replenishment use cases.
• Out of stock clicks are minimized
• AI and ML are genuinely woven into the international planning workflow where they earn their place, and deliberately not where they don't. The line between "the model says" and "the planner decides" is well drawn and trusted by both sides.
• The international/new market launch planning system gets faster, smarter, and more automated every quarter. Manual workarounds shrink; defensible automation grows.
• Planners trust the system. They challenge it when it's wrong, lean on it when it's right, and the feedback loop between operators and architecture is tight.
• The architecture you put in place scales. UK and EU today, but the next market launch is faster, smarter, and cheaper because of what you built.
• Quince's planning function is recognizably ahead of where comparable retailers are on AI-driven planning, and the right candidates from the outside want to come work on what we've built.

Key Responsibilities

• Define the forecasting and planning method for Quince's international expansion. What models live where, what AI capabilities sit on top, what humans own, how the pieces fit together end to end.
• Build and ship the early versions yourself. Prototypes in Python, SQL pipelines, Streamlit tools, LLM-powered workflows, agent prototypes. Get quick wins live in weeks, not quarters.
• Partner deeply with the data and BI team to harden, scale, and productionize what works.
• Work alongside PlanIt and existing planning workflows to build on top of it where it makes sense, and bridge gaps where it doesn't.
• Develop and own a real point of view on AI in planning. Where LLMs and agents add value (and where they don't), where classical models still win, what the next 12 months of capability look like, and how Quince stays ahead of it.
• Partner directly with the Senior Inventory Planner who operates the system day-to-day. Surface what the model is saying, why, and where it's unsure. Take their feedback and feed it back into the architecture.
• Build the analytical foundations the leadership team relies on. Forecast accuracy reporting, scenario modelling, ad hoc deep-dives that change how decisions get made.
• Codify the planning processes that don't exist yet. Translate them into systems where appropriate, into well-designed manual workflows where not.
• Continuously raise the bar on what good looks like for forecasting accuracy, planning automation and day to day AI integration.

Required

• 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 you know which planning problems are worth solving and which look interesting but aren't.
• 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 be able 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, you know what they're good for and what they break on, and you can hold your own in an architectural conversation about where they fit.
• 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 without losing either side.
• Excellent communicator. You can explain a model's limits to a non-technical leader, brief an engineer with enough detail that they want to work with you, and tell a planner 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. You've seen the launch-and-react planning rhythm up close.
• Familiarity with NetSuite or comparable ERP systems.
• Experience shipping LLM-powered or agentic workflows in a production or near-production context.
• A track record of cross-functional partnership with data engineering, ML, or platform teams, ideally as the planning-side counterpart they actually wanted to work with.
• Background in operations research, applied statistics, or formal forecasting training is a plus but not required.