Director of Decision Science

Stord•Atlanta, GA

About The Position

Stord is seeking a Director of Decision Science to build and lead a new function that leverages its unique and extensive data from the consumer pre- and post-purchase journey, warehouse operations, and parcel network. This role is crucial for turning data signals into competitive advantage through ML models for delivery prediction, carrier routing optimization, demand forecasting, churn analytics, and more. The Director will shape the function from the ground up, reporting to the VP of Data and partnering with the Head of AI. Success in the first year involves building the team, deploying a portfolio of productionized ML models with quantified business outcomes, establishing a robust experimentation framework, and ensuring business stakeholders actively use model outputs. The ideal candidate is a player-coach with strong technical ML and experimentation skills, leadership experience, and a deep understanding of business impact and adoption strategies. This role is for someone energized by building in a complex, real-world operational environment with rich, albeit not always clean, data.

Requirements

Practitioner-level ML skills: ability to design, build, and evaluate models (supervised learning, time-series, segmentation, recommendation systems, lift measurement).
Proficiency in experimentation methodology: designing experiments, sizing them correctly, accounting for confounders, and communicating results.
Experience with the full model lifecycle: taking models from raw data to production environments.
Comfort working with modern data platforms, including BigQuery or equivalent cloud warehouses, and familiarity with dbt or semantic layer concepts.
Player-coach commitment: willingness to be hands-on with model building.
Ability to develop junior talent and guide them to high performance.
Cross-functional credibility: ability to build trust with operations leaders, product managers, and engineers.
Framing model value in business outcomes (lift, cost per unit, margin improvement, retention).
Experience driving ML adoption in skeptical or immature environments.
Understanding of how Decision Science connects to revenue and cost.
Comfort with complex operational data including carrier events, warehouse throughput, order exceptions, and billing cycles.

Responsibilities

Design, develop, and productionize ML models that drive measurable operational outcomes, focusing on domains like delivery prediction, carrier routing optimization, demand and volume forecasting, exception management, and brand-level churn and performance analytics.
Build and own Stord's experimentation capability, including rigorous A/B test design, lift measurement, and causal inference.
Provide advanced analytics and segmentation to support product, operations, and commercial decisions, including customer and brand segmentation, behavioral analytics, and cohort analysis.
Ensure ML models are adopted and used by the business by translating outputs into actionable insights and workflows.
Build and lead a high-performing Decision Science function, including hiring, developing talent, and fostering a productive environment.
Partner with the Head of AI to ensure ML model outputs are accessible to AI-native products and to provide necessary model-driven signal for the AI roadmap.