Job description

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today s leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission. By combining comprehensive commerce-enablement technology with high-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator-built software suite including OMS, Pre- and Post-Purchase, and WMS platforms. Stord is leveling the playing field for all brands to deliver the best consumer experience at scale. With Stord, brands can increase cart conversion, improve unit economics, and drive sustained customer loyalty. Stord’s end-to-end commerce solutions combine best-in-class omnichannel fulfillment and shipping with leading technology to ensure fast shipping, reliable delivery promises, easy access to more channels, and improved margins on every order. Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe. Stord is backed by top-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures. Stord is looking for a Staff Data Scientist, a senior individual contributor to anchor data science efforts on the most difficult and highest-impact problems at Stord. You'll drive ML and MLOps technology strategy and embed with engineering teams to ship production models across our supply chain, fulfillment, and consumer-facing platforms. This is a dual-scope Staff role: part Architect, you'll define the data science and ML ops standards, tooling, and infrastructure the whole organization builds on, and part embedded Solver, you'll personally own and ship the highest-complexity modeling problems end to end. There are no direct reports. Your impact comes from technical depth and cross-team influence, at a scope and level of trust equivalent to Stord's senior leadership track. Stord's primary cloud and AI stack is Google Cloud Platform and Claude (Anthropic). You'll help shape how agentic AI gets built into the org's data and ML infrastructure, not just as a personal productivity tool, but as part of the standards and tooling you're already responsible for defining. Key Responsibilities Own complex modeling problems end-to-end, from framing through production deployment Conduct exploratory data analysis and build predictive models for supply chain, logistics, and consumer applications Define data science and MLOps standards, tooling, and infrastructure across the organization Embed with engineering teams to integrate models into production systems Lead technical strategy conversations with engineering and product leadership Help shape how agentic AI and AI-augmented workflows (governed semantic layers, agent-assisted model development, AI-augmented MLOps) are incorporated into Stord s data infrastructure Required Qualifications Technical Depth Expert-level Python with production code experience Strong SQL/Spark (Postgres, BigQuery) Statistical analysis and machine learning fundamentals, including causal inference and recommendation systems Production model deployment, monitoring, and retraining experience MLOps practices: model versioning, pipeline orchestration, drift detection Google Cloud Platform (BigQuery, Vertex AI, GKE/Kubernetes, Cloud Run), Stord s primary cloud and data stack Experience with or strong interest in agentic AI tooling for data science and ML ops, Stord s AI platform is Claude, and you ll help shape how agentic workflows get built into the org s data infrastructure Git/GitHub and collaborative engineering workflows Working Style Technical credibility and deep expertise in at least one hard technical domain Clear communication of complex concepts to engineering and business leadership Pragmatism and solution-focused delivery Self-direction in ambiguous, individual-contributor-track situations, this role has no direct reports; impact comes from technical depth and cross-team influence Preferred Qualifications Logistics, supply chain, or e-commerce background Recommendation systems or customer profile modeling at scale Real-time model serving and high-availability ML systems Elixir, TypeScript, or functional programming experience Kubernetes, CI/CD, and DataOps tooling familiarity

Requirements

Must-haves

Key Responsibilities Own complex modeling problems end-to-end, from framing through production deployment
Conduct exploratory data analysis and build predictive models for supply chain, logistics, and consumer applications
Define data science and MLOps standards, tooling, and infrastructure across the organization
Embed with engineering teams to integrate models into production systems
Lead technical strategy conversations with engineering and product leadership
Help shape how agentic AI and AI-augmented workflows (governed semantic layers, agent-assisted model development, AI-augmented MLOps) are incorporated into Stord s data infrastructure
Technical Depth Expert-level Python with production code experience
Strong SQL/Spark (Postgres, BigQuery)
Statistical analysis and machine learning fundamentals, including causal inference and recommendation systems
Production model deployment, monitoring, and retraining experience
MLOps practices: model versioning, pipeline orchestration, drift detection
Google Cloud Platform (BigQuery, Vertex AI, GKE/Kubernetes, Cloud Run), Stord s primary cloud and data stack
Experience with or strong interest in agentic AI tooling for data science and ML ops, Stord s AI platform is Claude, and you ll help shape how agentic workflows get built into the org s data infrastructure
Git/GitHub and collaborative engineering workflows
Working Style Technical credibility and deep expertise in at least one hard technical domain
Clear communication of complex concepts to engineering and business leadership
Pragmatism and solution-focused delivery
Self-direction in ambiguous, individual-contributor-track situations, this role has no direct reports; impact comes from technical depth and cross-team influence
Recommendation systems or customer profile modeling at scale
Real-time model serving and high-availability ML systems
Elixir, TypeScript, or functional programming experience
Kubernetes, CI/CD, and DataOps tooling familiarity

Nice-to-haves

Preferred Qualifications Logistics, supply chain, or e-commerce background

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaipythonsqlpostgresmachine learningkubernetestypescriptci/cd

Platform: workday
Effort: long