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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.
Stord is launching Stord Labs, an innovation center designed to evaluate emerging logistics technologies and redefine the future of fulfillment. We are seeking a highly skilled Lead Data Scientist to serve as the primary analytics and modeling expert within our core innovation team.
Unlike most data science roles in logistics that operate far removed from day\-to\-day operations, this role is embedded directly in a live fulfillment environment. As Lead Data Scientist for Stord Labs, you will build digital twins, develop predictive and prescriptive models, and evaluate agentic AI systems against real\-world warehouse workflows inside a dedicated micro\-fulfillment facility.
Your work will directly inform how innovations scale across Stord’s broader fulfillment network, translating experimental results into enterprise\-level operational strategies. You will serve as the technical backbone of a five\-person innovation team, partnering closely with controls engineers and operations specialists, and collaborating with frontier AI organizations and academic research partners.
If you bring deep expertise in simulation, machine learning, and applied analytics—and want to work at the intersection of data science and physical operations—this role is designed for you.
What You'll Do:
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Digital Twin \& Simulation Modeling
- Lead the design and development of digital twin models that accurately replicate end\-to\-end warehouse operations.
- Ingest and structure operational data from the micro\-fulfillment lab to build scalable macro\-simulations capable of representing enterprise\-scale environments with tens of thousands of SKUs.
- Stress test operational strategies—including slotting algorithms, multi\-pass picking, batching logic, and automation workflows—within simulation environments prior to production deployment.
Applied Artificial Intelligence
- Design, test, and deploy AI\-driven decision systems directly into operational workflows.
- Develop models for forecasting, labor planning, inventory optimization, task prioritization, and exception handling to improve throughput, speed, and cost efficiency.
- Build lightweight, production\-ready analytical tools and algorithms that improve operational performance without heavy infrastructure overhead.
Analytics \& Experimentation Validation
- Translate operational data into financial impact models, linking time\-and\-motion studies to margin improvement, productivity gains, and labor efficiency.
- Partner with operations analysts to design robust experimental frameworks, including success criteria, measurement methodologies, and statistical validation approaches.
- Analyze complex, multi\-variable experiments such as inventory commingling strategies and their impact on density, availability, and fulfillment speed.
Academic \& Frontier AI Partnerships
- Serve as the primary technical interface with external AI organizations, frontier model providers, and technology partners.
- Collaborate with academic institutions to sponsor applied research in simulation, optimization, and AI\-driven operations.
- Integrate external research and capabilities into real\-world operational testing within fulfillment workflows.
Basic Qualifications:
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- Master’s degree or PhD in Data Science, Operations Research, Computer Science, Industrial Engineering, or a highly quantitative field.
- 5\+ years of applied data science experience in supply chain, logistics, manufacturing, or other complex operational environments.
- Advanced proficiency in Python, R, and SQL.
- Proven experience building discrete\-event simulations, continuous simulations, or digital twin systems using tools such as AnyLogic, Simio, FlexSim, or custom frameworks.
- Strong track record of deploying machine learning and optimization models into live production or operational decision systems.
Bonus Points:
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- Experience operating as a standalone data scientist in an R\&D lab, innovation center, startup environment, or advanced manufacturing technology setting.
- Familiarity with WMS/OMS data structures and warehouse operational datasets.
- Experience experimenting with large language models (LLMs) or agentic AI systems for workflow automation, exception management, or decision support in operations contexts.
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.
Frequently Asked Questions
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