Supply Chain AI Jobs Are Here | U.S. Hiring Research
Overview
You’re probably wondering what jobs AI will create in supply chain and in other places. Artificial intelligence (AI) is taking on a heightened role in job creation, especially in the field of supply chain management, as organizations rethink planning, optimization, and execution in response to how generative AI is reshaping supply chains. Supply chain leaders have been early adopters of AI since robotics, machine learning, and automation were immediate precursors to artificial intelligence. It makes sense that AI roles took hold quickly across the end-to-end supply chain. Demand for tech savvy talent in supply chain has always been high, but it’s now getting more consumed with the need for AI fluency and leaders who can manage tech and AI talent.
Employers demonstrated an increased need for professionals who can use AI for forecasting, optimization, and automation. Trend analyses in 2025 indicate that hiring priorities reflect an increased need for mid to senior level AI talent with both data science expertise and supply chain domain knowledge. Rate of adoption for these positions, however, will likely vary greatly depending on the size of the company, ability to implement enterprise level AI modules, and of course private vs. public sector influence. But just because you don’t work for a Fortune 15 doesn’t mean you can’t have a major influence on the growth of technology and AI platforms for a smaller company.
Join us as we summarize some of the most common AI-related job titles in US supply chain fields, their prevalence, and recent examples, along with the insights on how these roles are evolving and what talent tactics are necessary to attract and retain the best candidates in these roles.
Common AI-Related Job Titles in Supply Chain
1. Supply Chain Data Scientist / Analytics Scientist:
By far one of the most prevalent roles, data scientists apply machine learning and advanced analytics to supply chain problems like demand forecasting, inventory optimization, and logistics routing. These roles bridge traditional operations research with modern AI. Major retailers, manufacturers, and logistics firms are hiring data scientists to improve supply chain efficiency. For example, Target recently listed a Lead Data Scientist – Supply Chain Optimization (focused on applied ML and simulation) , and Boeing advertised multiple Supply Chain Data Scientist positions in November 2025 . Such roles typically require strong Python/ML skills plus knowledge of supply chain processes. Recruiting reports note that “Directors of Data Science” focused on supply chain (including generative AI initiatives) are in high demand . Indeed, many enterprises (Amazon, Walmart, FedEx, etc.) are investing in predictive analytics and AI-enabled planning tools and building data science teams around supply chain use cases .
2. Machine Learning Engineer (Supply Chain Focus):
These are technical specialists who design, train, and deploy AI/ML models within supply chain contexts. They often work on algorithms for supply chain automation, predictive maintenance, or route optimization. Companies developing AI-driven supply chain platforms or internal tools recruit ML Engineers with domain knowledge. For instance, one recent listing was AI/ML Engineer (Select Source International) to lead development of AI solutions across R&D and supply chain functions . Similarly, an ML Engineer for demand forecasting is cited as a key role in logistics and retail sectors . ML Engineers in this field collaborate with supply chain analysts and IT teams to integrate models into inventory systems, warehouse robotics, or transportation management. These roles underscore the trend that AI talent who “speak both Python and production lines” (i.e. understand the operational context) are highly sought .
3. AI Solutions Architect / Specialist (Supply Chain):
As companies implement AI in supply chain operations, they are hiring specialists to bridge the gap between emerging AI technologies and real-world supply chain processes. These titles can vary (e.g. AI Supply Chain Optimization Specialist, AI Integration Manager, etc.), but they focus on deploying AI tools and ensuring they align with business needs. A notable example is AI Business Integration Specialist at an SC logistics firm, responsible for “bridging emerging AI technologies and real-world operations” . Likewise, GE Healthcare recently sought an AI Solutions Leader – Integrated Supply Chain to drive AI initiatives within its supply chain team . Such roles require both technical AI understanding and supply chain expertise, serving as the “technical bridge” between data science teams and operations . They often lead pilot projects using AI for demand sensing, warehouse automation, or transportation optimization. This is a relatively new category: one career guide describes the “AI Supply Chain Optimization Specialist” as a new role at the intersection of data science and supply chain management, focused on AI-driven solutions for demand, inventory, and logistics .
4. Supply Chain Analyst / Manager (with AI & Analytics):
Traditional supply chain analyst and planning roles are evolving to require data analytics and AI familiarity. Many job postings for Supply Chain Analysts now list experience with machine learning or AI tools as a plus, as companies augment forecasting and planning with AI. While the title “Supply Chain Analyst” remains common, the skillset is shifting – e.g. using predictive models rather than just Excel. In 2024, over half of supply chain job postings required some software or data skills , and a subset explicitly seek skills in AI/automation. For example, Instacart advertised a Senior Business Systems Analyst – Supply Chain noting exposure to emerging AI capabilities for supply chain optimization as a desired qualification . Similarly, Gap Inc.’s sourcing analytics group looks for managers with degrees in AI or data science alongside supply chain expertise . In practice, many supply chain managers are upskilling with AI: industry surveys show a “spike in demand for AI specialists and data scientists” in supply chain teams undergoing digital transformation . Even if “AI” isn’t in their titles, analysts and managers increasingly use AI-driven software (for scenario planning, risk management, etc.), blurring the line between classic analyst roles and AI roles.
5. Product Manager – AI Supply Chain Solutions:
As AI is embedded into supply chain software and services, companies (especially tech providers and startups) are hiring product managers and consultants to develop these AI-driven supply chain solutions. For example, enterprise AI firms like C3.ai list roles such as Senior Product Manager – Supply Chain to build “intelligent, proactive AI” capabilities into supply chain products . Another startup posting in late 2025 was for a Founding Software Engineer, AI for Supply Chain Automation (Together For Talent), highlighting the blend of software development and supply chain domain knowledge in AI startups . Consultancies and solution providers might use titles like AI Supply Chain Consultant or Digital Supply Chain Transformation Manager, focusing on implementing AI tools for clients. These roles reflect how AI and advanced analytics are becoming core to supply chain software offerings, requiring leaders who understand both the technology and the logistics domain. Hiring data shows growth in titles like “Solutions Architect – AI in Supply Chain” for integrating AI into ERP/MES or logistics systems .
6. Automation & Robotics Roles:
AI-driven automation in warehouses and logistics has spawned roles that, while sometimes labeled under “automation” or “robotics,” are closely related to AI. Examples include Robotics Process Engineer or Warehouse Automation Manager (implementing autonomous robots, computer vision systems, etc.), and AI Robotics Technician. These titles may not always contain “AI,” but they leverage AI technologies (for instance, using machine vision for sorting or autonomous guided vehicles). Companies like Amazon Robotics have dozens of openings for engineers in warehouse automation . Additionally, Operations Research Scientist/Engineer is a long-standing supply chain role that now frequently involves AI/ML methods for optimization. (For instance, Amazon and UPS hire operations research scientists who use ML for network optimization.) We include these roles as “AI-related” since they deploy AI algorithms for automation and decision support in supply chain contexts.
Frequency Note: Exact frequency rankings are fluid, but Data Scientist positions appear especially common, with many active postings across industries. On ZipRecruiter, a search for “Data Scientist – Supply Chain” returns numerous openings nationwide (from retail giants to healthcare systems) . Meanwhile, Indeed’s listings for “AI Supply Chain” exceed 170,000 (though this includes a broad set of jobs where AI is mentioned) . In late 2024, only ~1.6% of supply chain jobs explicitly referenced AI – indicating that dedicated AI-in-title roles are still emerging. However, by 2025, AI-centric roles in supply chain are multiplying quickly, especially in larger organizations and tech firms. For example, Amazon’s Supply Chain division has roles like “Sr. Cost Analyst, AWS AI/ML Supply Chain” (focusing on cost modeling for AI hardware) , and startups like Vorto are recruiting Senior AI Engineers to build autonomous supply chain platforms . Overall, “Data Scientist” and “Analyst” titles (with AI/ML skills) remain the most common, but titles denoting AI leadership (e.g. AI Solution Lead, AI Specialist) are on the rise.
Examples of Recent Job Listings (Aug–Nov 2025)
To illustrate, here are a few active U.S. job postings (from the last ~3 months) that highlight AI-related supply chain roles. Each includes the title, employer, and a reference link:
- Founding Software Engineer – AI for Supply Chain Automation, Together For Talent (SF) – An early-stage startup role building intelligent software to manage global goods movement . (Posted Oct 2025)
- Data Scientist – Supply Chain Management, Tata Consultancy Services (Seattle) – Requires experience with generative AI frameworks and analytics applied to procurement, inventory, finance, etc. . (Active Fall 2025)
- Senior Product Manager – Supply Chain (AI), C3 AI (Redwood City, CA) – Driving development of AI-powered supply chain solutions, working with customers on intelligent forecasting and optimization . (Active Nov 2025)
- AI Business Integration Specialist (Supply Chain), RK Logistics Group (Fremont, CA) – Focused on “bridging emerging AI technologies and real-world business operations” in the supply chain domain . (Posted Nov 2025)
- AI/ML Engineer (Supply Chain projects), Select Source Intl. (Deerfield, IL) – A technical lead to deploy machine learning solutions across R&D and supply chain operations . (Recent posting)
- Sr. Cost Analyst – AWS AI/ML Supply Chain, Amazon (Seattle, WA) – An analyst role within Amazon’s AI/ML hardware supply chain, responsible for consolidating material costs for AI products . (Active Oct 2025)
- AI Solutions Leader – Integrated Supply Chain, GE HealthCare (Waukesha, WI) – Senior role to champion AI solution implementation in the healthcare equipment supply chain . (Posted within last 30 days)
Notice how the postings each list how employers are specifying expertise in AI as part of the supply chain responsibilities. The GE role is very clear about the AI hiring priority. The Amazon role combines cost analysis with AI product knowledge. Also, other supply chain positions like Supply Chain Manager or Planner are mentioning AI exposure. A supply chain planner contract role in Washington notes it’s an “AI-driven environment” with the possibility of full-time hire.
| Job Title / Category | Primary Focus in Supply Chain | Typical Use Cases |
|---|---|---|
| Supply Chain Data Scientist / Analytics Scientist | Applied ML and advanced analytics for supply chain decisions | Demand forecasting, inventory optimization, network modeling, logistics analytics |
| Machine Learning Engineer (Supply Chain) | Building and deploying ML models into production systems | Forecasting pipelines, predictive maintenance, routing optimization, model deployment and MLOps |
| AI Solutions Architect / AI Specialist (Supply Chain) | Translating AI capabilities into operational solutions | AI tool integration, pilots, workflow alignment, value realization and adoption |
| AI Business Integration Specialist / AI Integration Manager | Connecting AI initiatives to business processes and stakeholders | Change enablement, requirements, process redesign, governance for AI programs |
| Supply Chain Analyst / Manager (AI-skilled) | Using AI-enabled tools for planning and performance improvement | Scenario planning, demand sensing, control-tower insights, risk monitoring |
| Product Manager, AI Supply Chain Solutions | Defining and delivering AI-powered supply chain products | Roadmaps, customer discovery, feature design for planning, visibility, optimization tools |
| Operations Research Scientist / Optimization Engineer | Quantitative optimization, simulation, decision science | Network design, inventory policy, capacity planning, transportation optimization |
| Warehouse Automation Manager / Robotics Process Engineer | AI-enabled automation in distribution and fulfillment | Robotics deployment, computer vision quality checks, autonomous systems, throughput improvement |
| Supply Chain AI Lead / Head of Supply Chain AI | Leadership for AI strategy and execution across supply chain | Portfolio prioritization, team building, vendor selection, KPI ownership and scaling |
Trends in Terminology and the Emerging Positions
New hybrid job titles are emerging, showing the need for AI skill requirements. For instance, professional associations now refer to certifications for “AI Supply Chain Analyst/Manager,” signaling that these terms are entering the lexicon of supply chain careers. While many companies, in practice, still use traditional titles like Analyst, manager, Engineer, but emphasize AI in the job description. However, there are AI specific job titles like “AI Specialist – Supply Chain” or “Supply Chain AI Lead” starting to surface in job boards.
Similarly, startups and tech-forward firms use titles such as “Director of Supply Chain Data Science” or “Head of Supply Chain AI” for leadership roles overseeing these efforts.
Supply chain roles are not necessarily being replaced, but redefined. Upskilling with AI tools is becoming commonplace among analysts and planners. Using machine learning for predictive demand planning is becoming more commonplace and demonstrates supply chain’s broader influence as early adopters of technology. The companies that replaced analog historical analysis with machine learning saw a spike in production and efficiency.
At the Supply Chain and Logistics Institute at Georgia Tech, instructors emphasize the importance of AI readiness for talent at every level of the supply chain. They refer to it as the “AI Ladder” and entry level, mid-career, and senior level supply chain professionals are expected to climb this ladder, integrating AI-driven analytics into their work as a way to stay ahead. This has also led to companies with advanced digital strategies to form dedicated teams for supply chain AI initiatives, often under titles like Supply chain Innovation, Digital Supply Chain or Advanced Analytics. These teams hire data scientists, ML engineers, and product managers specialized in supply chain use cases.
Another notable trend is the focus on domain-specific AI roles. Rather than hiring generic AI engineers, companies want talent who understand the nuances of supply chain. A 2025 labor survey noted “companies want AI talent with a deep understanding of the industries they operate in”, such as logistics . Thus, job titles increasingly pair AI/ML with supply chain context (e.g. “ML Engineer – Supply Chain Forecasting”). Even senior roles like Senior Director of Supply Chain Operations now explicitly call out openness to leveraging AI tools as part of the job.
Growth and Outlook
According to Indeed, genAI mentions in job postings surged from September 2023 to 2024. Supply chain logistics and support sectors initially lagged in AI postings. But the gap is closing as more supply chain functions begin implementing and experimenting with artificial intelligence. Supply chain organizations have started investing in roles that augment and complement human supply chain functions with AI. These same companies have started financially prioritizing roles that can be easily enhanced with the addition of AI.
Early adopters of AI in supply chain have reported significant performance gains which can lead to more hiring in this arena. There’s also an observable shift in hiring toward experienced professionals.
- 85% of AI job openings in 2025 targeted mid to senior level roles
This implies that positions such as AI Supply Chain Director, Lead Data Scientist, etc., will be more prominent than entry-level titles. But that is still yet to be determined.
In terms of terminology, “digital supply chain” and “automation” remain buzzwords alongside AI. Some companies frame AI roles under broader digital transformation initiatives (e.g. Supply Chain Digital Analyst or Automation Engineer), which are effectively AI-related. Over time, as AI becomes more ingrained, the distinction may blur – tomorrow’s “Supply Chain Manager” might be assumed to handle AI-driven systems by default. For now, though, organizations are explicitly seeking out specialized job titles to lead AI integration in supply chain functions.
Recruiting Firms Are Adapting to Supply Chain AI Hiring
As AI-driven roles accelerate across supply chain organizations, recruiting capabilities are evolving alongside them. Identifying talent who can operate at the intersection of supply chain execution, analytics, and artificial intelligence requires more than keyword matching or generic technical screening.
In response, SCM Talent Group has invested in the formal launch of a dedicated AI & Analytics recruiting practice focused on technology roles that intersect with end-to-end supply chain disciplines, including operations, logistics, and manufacturing.
By combining traditional supply chain recruiting expertise with advanced analytics fluency, this approach is designed to help employers assess not only technical AI capability, but also real-world operational impact, ensuring AI talent can translate models, insights, and automation into measurable supply chain outcomes.
(Learn more about SCM Talent Group’s AI & Analytics recruiting capabilities here: https://scmtalent.com/ai-analytics-recruiters)
Conclusion
Summary of Top Roles: The landscape of AI in supply chain hiring can be summarized by a few key titles and categories: Data Scientists (applying AI to supply chain analytics) are the most common, followed by Machine Learning Engineers (developing AI solutions for logistics problems). Traditional Analyst/Manager roles are being upskilled with AI competencies, and new specialist titles like AI Supply Chain Specialist, AI Solutions Architect, or Supply Chain AI Product Manager are emerging at the nexus of tech and operations.
These roles underscore a shift toward data-driven, proactive supply chain management. As one report noted, “early adopters of AI in supply chain have gained a competitive edge,” prompting a wave of hiring for the talent to replicate those gains . Companies are clearly preparing for an AI-augmented supply chain workforce: investing in both technology and people who can harness it . In summary, if current trends hold, AI-related job titles in supply chain will only become more common, with naming conventions likely standardizing as these positions mature. Whether called “Supply Chain AI Analyst” or “Logistics Data Science Lead,” the core theme is the same: blending supply chain domain expertise with AI skills is now one of the hottest tickets in the U.S. job market for operations professionals .
Sources: Recent job postings on Indeed, LinkedIn, ZipRecruiter; industry reports and hiring trend analyses (2024–2025) etc., as cited throughout. All examples and statistics are drawn from the last 12–18 months of available data to ensure currency.
