Director of Decision Science

Stord runs on Google Cloud Platform and Claude (Anthropic) is our primary AI platform company-wide. This function wont just use AI tools youll 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