Staff Data Scientist

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. About the Staff Data Scientist Position 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. 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