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About This Role
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
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Tackle the Hardest Problems
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- Own the most complex, ambiguous, and high\-stakes modeling problems at Stord end\-to\-end, from initial framing through production deployment
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- 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
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- 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
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- 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
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- 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
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Required Technical Skills
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- 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
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- 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
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- 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
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At STORD Warehouse, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
STORD Warehouse AI Hiring
STORD Warehouse has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Atlanta, GA, US, Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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