Full Job Description
This is Stord's first dedicated Decision Science leadership role, a hands-on Data Science Director role for someone who wants to build a full-stack analytics and machine learning function from the ground up. Stord processes $10B+ in commerce annually across fulfillment, warehousing, and software platforms, generating rich datasets spanning consumer behavior, warehouse operations, and parcel networks. You'll turn that data into a competitive advantage: owning the path from raw data to production model to adopted business decisions, not just delivering dashboards or one-off analyses.
You'll build on Stord's modern data stack (GCP, BigQuery, dbt) and help define how the Decision Science function adopts Claude and agentic AI as core infrastructure, not just a productivity add-on, but part of how the team scales analytics, experimentation, and machine learning across the business.
**How We Work**
Stord runs on Google Cloud Platform, and Claude (Anthropic) is our primary AI platform, company-wide. This function won't just use AI tools, you'll help define how a modern Decision Science team works alongside agentic AI: governed metrics and semantic layers that agents query first, documented "skills" for safe self-service analytics, and clear human ownership of metric definitions and high-stakes model review. If you want to build a data organization from the ground up on a genuinely AI-forward stack, this is a rare opportunity to set that foundation.
**Key Responsibilities:**
**Machine Learning Portfolio:**Design and productionize models in delivery prediction, carrier routing, demand forecasting, exception management, and churn analytics, owning the full model lifecycle from notebook to production
**Experimentation Platform:** Build a self-serve A/B testing and causal inference platform the broader business can use independently, not a one-off analysis service.
**Advanced Analytics:** Conduct segmentation, behavioral analysis, and cohort analysis supporting product and operations decisions.
**AI-Augmented Decision Science:** Partner with the Head of AI to integrate model outputs into AI-native products, and shape how the team uses agentic AI (governed semantic layers, agent-based querying, skill/prompt documentation) to automate routine analysis and free the team to focus on forecasting, causal inference, and ML.
**ML Adoption:** Ensure models drive actual business decisions, translating outputs into actionable workflows, not dashboards.
**Team Leadership:** Hire, develop, and lead a high-performing Decision Science team as a player-coach: hands-on individually while building the team around you.
**Year 1 Success Criteria**
- Team staffed and contributing
- Five+ production models with quantified business outcomes
- Live, adopted experimentation platform
- Business stakeholders actively using model outputs
- Full commerce data stack (consumer, fulfillment, parcel) actively modeled
- Year 2 roadmap defined with organizational buy-in
**Required Qualifications:**
**Technical Depth**
- Practitioner-level machine learning: design, build, and evaluate models independently, end to end (notebook to production)
- Expertise in supervised learning, time-series forecasting, segmentation, recommendation systems, and lift measurement
- Strong experimentation design and causal inference skills, with the statistical fluency to communicate results to non-technical stakeholders
- Proficiency with GCP and BigQuery (or a comparable cloud data warehouse); hands-on experience with dbt, semantic layers, and modern ML tooling (BigQuery ML, Vertex AI, or equivalent)
- Familiarity operating in or building an LLM-augmented analytics environment, e.g., semantic layers, agent-based querying, prompt/skill documentation, or comparable AI-assisted self-service analytics tooling (Claude, Anthropic, or similar)
**Leadership**
- Player-coach mindset: hands-on in a small team environment, with a track record of developing junior talent
- Ability to establish cross-functional credibility and drive ML/AI adoption in skeptical or immature data environments
- Fluency translating technical work into business language, lift, cost per unit, margin, retention - not statistical jargon
**Business Instinct**
- Clear understanding of how Decision Science connects to revenue and cost
- Ability to build and defend a team roadmap in budget and planning conversations
**Preferred Qualifications:**
- Experience operating at the intersection of physical operations and software (supply chain, logistics, 3PL, fulfillment, retail ops, or similar "unglamorous," real-world data environments)
- Comfort with messy, real-world operational data rather than a mature, pre-cleaned platform
- Track record driving adoption of models and experimentation in complex, multi-stakeholder organizations
- Experience going from individual contributor to Director, or building a data science function from zero to one
