Director of Decision Science

Stord, Inc.; Atlanta (GA); On-site; USD 180,000 - 250,000; Full time

Job summary

Stord, Inc. is seeking an energetic expert to establish the first dedicated Decision Science leadership role. You will shape the function from the ground up, reporting to the VP of Data and collaborating with the Head of AI to operationalize ML across the business.

This role requires hands-on leadership and the ability to drive measurable outcomes in a fast-growing logistics environment. The successful candidate will build and lead a high-performing Decision Science team, drive adoption of model

Qualifications

• This role requires someone empowered by operational complexity: carrier events, warehouse throughput, order exceptions, billing cycles, brand performance.

Responsibilities

• Design, develop, and productionize ML models that drive measurable operational outcomes.
• Build and own the experimentation framework with rigorous A/B tests.
• Own advanced analytics and segmentation to support product, operations, and commerce.
• Lead and build a high-performing Decision Science team.
• Collaborate with the Head of AI to make model outputs accessible to AI-native products.

Job description

Stord is rapidly scaling and seeks an energetic expert to help achieve our mission. 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.

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, exception management, and brand‑level churn and performance analytics.
• Experimentation framework – Build and own Stord’s experimentation capability, including rigorous A/B test design, lift measurement and causal inference where appropriate.
• 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 by translating outputs into language and workflows the business acts on. Adoption is half the job.
• Team – Build and lead a high‑performing Decision Science function. Hire well, develop people, and create an environment where strong data scientists excel.
• AI partnership – Work alongside the Head of AI to make ML model outputs accessible to AI‑native products and ensure the AI roadmap benefits from model‑driven signals.

What Success Looks Like in Year1

• The team is staffed and operating well – data scientists are hired, onboarded, and contributing at pace.
• A portfolio of ML models is in production – aiming for five or more models in live operational or commercial contexts, each with a quantified business outcome.
• An experimentation framework is live and adopted – Operations and Product teams can run and interpret A/B tests independently.
• Business stakeholders across Operations and Commerce actively use model outputs in decisions.
• The full commerce data stack – consumer, fulfillment, and parcel – is being actively modeled, not just the most obvious domain.
• The Decision Science roadmap for Year2 is defined, credible, and has organizational buy‑in.

What We Are Looking For

You are a player‑coach: depth to design and build models yourself and the leadership instinct to grow a team that does it without you.

Technical Depth

• Practitioner‑level ML – design, build, and evaluate models (supervised learning, time‑series, segmentation, recommendation systems, lift measurement).
• Experimentation methodology – design proper experiments, size, account for confounders, and communicate results in plain English.
• Full model lifecycle – bring models from raw data to production, understanding the gap between notebooks and production environments.
• Modern data platforms – comfortable with BigQuery or equivalent, familiar with dbt or semantic layer concepts.

Leadership

• Player‑coach commitment – hands‑on, not remote management.
• Develops junior talent – elevate capable data scientists.
• Cross‑functional credibility – build trust with operations, product, and engineering leaders.

Commercial and Business Instinct

• Business‑language first – frame model value in outcomes the business cares about (lift, cost per unit, margin improvement, retention).
• ML adoption as a change‑management mission – treat adoption as sales and change management rather than purely technical.
• Connected to commerce – understand how Decision Science impacts revenue and cost; make the case for the roadmap in budget conversations.

Role Requirements

This role requires someone empowered by operational complexity: carrier events, warehouse throughput, order exceptions, billing cycles, brand performance. It is not suited for those seeking a clean data model before building.

Stord is an equal‑opportunity employer and values diversity. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Stord participates in E‑verify and will provide the federal government with your Form I‑9 information to confirm that you are authorized to work in the U.S.