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 Role:Stord is building ML capabilities that directly power our cloud-based supply chain platform, which handles over $10B in commerce annually. You'll work alongside a Machine Learning Engineer to own the full data science lifecycle — from exploratory analysis and model development through production deployment and ongoing performance improvement. This is a high-impact, hands-on role on a small team where your work will directly shape how millions of shipments are planned, routed, and fulfilled.
As a Senior Data Scientist at Stord, you will own the scientific rigor behind our ML features — designing experiments, developing models, and translating complex data findings into actionable product decisions. You'll work closely with an ML Engineer who owns productionization, but you'll remain deeply involved through deployment, monitoring, and iteration. This is a rare opportunity to apply data science directly to hard logistics problems with immediate, measurable customer impact.
What You'll Do
Data Analysis & Problem Framing
Conduct exploratory data analysis to validate assumptions, surface insights, and identify data quality issues before they affect model development
Answer specific business questions with rigorous, data-driven analysis
Define success metrics and evaluation frameworks in collaboration with product and engineering stakeholders
Translate ambiguous business problems into well-scoped data science problems
Model Development
Design, build, and evaluate predictive models for logistics use cases: delivery time estimation, demand forecasting, routing optimization, capacity planning
Own model quality — feature selection, validation methodology, bias detection, and performance benchmarking
Run structured experiments to validate improvements before production promotion
Contribute to improving existing production models using performance data and operational feedback
Production Involvement
Write production-quality Python code, not just notebook prototypes
Partner closely with the ML Engineer through deployment — your involvement doesn't end at handoff
Monitor model performance in production and own the scientific response to drift or degradation
Contribute to A/B testing design and interpretation of results
Technical Translation & Collaboration
Communicate model behavior, limitations, and tradeoffs clearly to engineers, product managers, and business stakeholders
Serve as Stord's subject matter expert in data science and ML — you are expected to lead this domain, not just contribute to it
Present findings, recommendations, and model decisions to leadership and executives, translating technical complexity into business impact and strategic context
Document technical decisions in ways accessible to non-data scientists
Participate in sprint planning, code review, and architectural decisions for AI/ML features
Help other engineers build intuition around statistical methods and ML approaches
What You'll Need
Required
5+ years of data science experience with models shipped to and maintained in production
Expert-level Python — production code, not just analysis scripts
Strong SQL — complex queries, performance optimization, BigQuery and Postgres experience
Deep understanding of statistical fundamentals and ML model evaluation
Experience with cloud ML platforms, preferably GCP
Familiarity with logistics, e-commerce, fulfillment, or supply chain domains — you understand what on-time delivery, carrier performance, and demand variability actually mean operationally
Bonus
Experience working embedded with software engineering teams rather than in a traditional data science org
Familiarity with feature engineering for real-time inference
Elixir or TypeScript exposure, or comfort operating in polyglot engineering environments
CI/CD and DevOps familiarity
Experience with monitoring tools (Datadog or equivalent) applied to model performance
Contributions to model improvement, not just greenfield development
