Staff Data Scientist

About the role

Stord is revolutionizing the logistics industry with our cloud-based supply chain platform. We empower brands to compete and grow by providing end-to-end logistics solutions coupled with our modern platform of tools covering Order Management (OMS), Warehouse Management (WMS), Consumer Experience (Pre/Post Purchase), Demand Planning, and more. As we continue to enhance our platform and look to the future, we are doubling down on our investment in Data and ML to make our platform even more powerful for the brands that use it.

What You'll Do

• Tackle the Hardest Problems
• Own the most complex, ambiguous, and high-stakes modeling problems at Stord end-to-end, from initial framing through production deployment
• Conduct deep exploratory data analysis to validate assumptions and surface non-obvious insights
• Build predictive models for supply chain optimization and consumer-facing applications, including delivery time estimation, demand forecasting, routing optimization, personalized product recommendations, and customer profile enrichment and segmentation
• Write production-quality code that integrates cleanly with existing services and can be maintained by others
• Drive the Technology Stack & Standards
• Play a leading role in defining Stord's data science and ML technology stack, tooling, and infrastructure choices
• Work alongside fellow data scientists and ML ops to establish standards and best practices for model development, deployment, monitoring, and retraining
• Contribute to both the data science and ML ops sides of the stack as needs arise
• Document technical decisions and patterns in ways the broader team can build on
• Partner Directly with Engineering
• Embed with engineering teams to integrate models into production systems and ship features
• Work with engineers to deploy models as microservices or API endpoints and own their performance over time
• Participate in sprint planning and agile ceremonies
• Review code and provide feedback on data-related implementations
• Engage with Leadership
• Lead technical conversations with engineering and product leadership on data science strategy and investment
• Translate complex modeling approaches and tradeoffs into clear, actionable recommendations for non-technical stakeholders
• Identify high-leverage opportunities for data science across the platform and bring them forward with supporting analysis

What You'll Need

• Required Technical Skills
• Expert-level Python programming with production code experience
• Strong SQL skills with Postgres and BigQuery experience
• Deep understanding of statistical analysis and machine learning fundamentals
• Proven experience deploying and operating models in production environments, including monitoring and retraining
• Hands-on experience with ML ops practices: model versioning, pipeline orchestration, drift detection, and experimentation frameworks
• Experience with cloud platforms (AWS, GCP, or Azure)
• Proficiency with Git/GitHub and collaborative development workflows
• Required Soft Skills
• Technical credibility - earns trust as the expert on hard problems through demonstrated depth, not just seniority
• Communication - carries technical opinions clearly into leadership conversations and can make complex tradeoffs legible
• Pragmatism - focuses on delivering working solutions and iterates; doesn't wait for perfect conditions
• Collaborative - works openly with data scientists, ML engineers, and software engineers toward shared outcomes
• Self-directed - identifies what needs to be done in ambiguous situations without waiting for detailed specs
• Preferred Qualifications
• Background in logistics, supply chain, or e-commerce domains
• Experience building recommendation systems or customer profile modeling at scale
• Experience with real-time model serving and high-availability ML systems
• Experience with Elixir, TypeScript, or functional programming paradigms
• Familiarity with Kubernetes, CI/CD, and DataOps tooling
• Experience helping define standards or tooling choices across a data science team