Über
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. We are seeking a Staff Data Scientist to serve as a technical anchor across our data science efforts. This is a senior individual contributor role where you will work on the most difficult and highest-impact problems at Stord, drive the direction of our data science and ML technology stack, and help set standards and best practices alongside fellow data scientists and ML engineers. You'll work directly with engineering teams embedded in product development, and you'll regularly engage with leadership to shape how we invest in and apply data science across the platform. In this role, you will be expected to move fluidly across data science and ML ops depending on where you're needed most. You'll work on areas such as demand forecasting, delivery date estimation, pricing analytics, network simulation, customer recommendations, and customer profile management while also helping define how we build, deploy, and maintain models at scale. This is a role for someone who thrives on hard problems, brings strong technical opinions, and can carry those opinions credibly into conversations with both engineers and executives. 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
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
Sprachkenntnisse
- English
Hinweis für Nutzer
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