Director of Decision Science

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.

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.

• 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

• 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)

• 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

• Clear understanding of how Decision Science connects to revenue and cost

• Ability to build and defend a team roadmap in budget and planning conversations

• 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