Director of Decision Science

The Opportunity

Stord is the commerce enablement platform that powers $10B+ in commerce annually for some of the world's leading brands. We sit at the intersection of physical operations and software - running fulfillment centers, parcel networks, and the technology stack that ties it all together.

Decision Science is the function that turns that signal into competitive advantage. The modeling opportunities here are genuinely rich: delivery prediction, carrier routing optimization, demand and volume forecasting, brand-level churn and performance analytics, exception management, personalization. The opportunity is to build a function that develops models the business trusts, adopts, and acts on - and that makes Stord smarter with every order we process.

This is the first dedicated Decision Science leadership role at Stord. You will shape the function from the ground up, reporting to the VP of Data, and working in close partnership with the Head of AI. The two functions are complementary - Head of AI owns AI-native product capabilities; you own the model-driven insights and operational intelligence that power both the product we sell and the decisions we make internally.

What You'll Own

• ML model portfolio - Design, develop, and productionize ML models that drive measurable operational outcomes. Priority domains include delivery prediction (EDD), carrier routing optimization, demand and volume forecasting, brand-level churn and performance analytics.
• Experimentation framework - Build and own Stord's experimentation capability. That means rigorous A/B test design, lift measurement, causal inference where appropriate, and a framework the rest of the business can use to run experiments without coming to your team for every one.
• Advanced analytics and segmentation - Own the analytical depth that supports product, operations, and commercial decisions - customer and brand segmentation, behavioral analytics, cohort analysis
• ML adoption - Ensure models are actually used. This means translating outputs into language and workflows the business acts on, not publishing results to a dashboard no one reads. Adoption is half the job.
• Team - Build and lead a high-performing Decision Science function. Hire well, develop the people you have, and create an environment where strong data scientists do their best work.
• AI partnership - Work alongside the Head of AI to ensure ML model outputs are accessible to AI-native products and that the Head of AI's roadmap has the model-driven signal it needs to be effective.

What We Are Looking For

• Player-coach commitment - willingness to be hands-on is non-negotiable. This is a small team. You cannot manage from a distance.
• Develops junior talent - you can take a capable data scientist and make them better. You know what good looks like and how to close the gap.
• Cross-functional credibility - you build trust with operations leaders, product managers, and engineers who are not data people. They need to believe in your models before they will change how they work.
• Business-language first - you frame model value in outcomes the business cares about, not statistical metrics. Lift, cost per unit, margin improvement, retention. Not precision-recall curves.
• Awareness of operational complexity - you see the operational richness as an advantage, not a complication. You are energized by building in that environment.