Staff Data Scientist

About the role

As a Staff Data Scientist at Stord, you will play a pivotal role in driving the strategic direction of our data science and machine learning technology stack. Your responsibilities will span from tackling the most complex and high-stakes modeling problems to integrating models into production systems and engaging with leadership to shape data science strategy.

Responsibilities

• 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

Requirements

• 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
• 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