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About This Role
BUILD A BETTER CAREER WITH MSC
Serving customers and our community starts with the best people doing their best work. That is precisely what we have created at MSC Industrial Supply Co. We are a leading North American distributor of metalworking and maintenance, repair, and operations (MRO), products and services. We offer more than 2 million products and have over 80 years of experience across dozens of industries. We aim to execute our Built to Make You Better brand promise for all our stakeholders, including our associates.
Requisition ID :21723
Employment Type : Full Time
Job Category : Digital/IT
Work Location : Davidson, NC (CSC)
BRIEF POSITION SUMMARY:
At MSC, we need a single, prioritized view of where AI will create value and a disciplined way to decide how AI will fit into business processes. The Director, AI Product Management owns the enterprise AI roadmap and the AI use-case portfolio and is accountable for turning business demand into a sequenced set of 3rd party AI products, agent workflows, and vendor solutions with clear business cases and measurable outcomes.
This position partners closely with business leaders, Finance, Procurement, Legal, Cybersecurity, Technology, and Data to run intake and prioritization, vet AI vendors, and design orchestrated processes that combine people, systems, and agents. The role works hand in hand with the Director, AI Governance & Operations, who owns controls, evaluation, and value tracking after implementation.
The Director, AI Product Management acts as the single owner of what gets done and why. This leader brings structure to AI demand, makes clear build-versus-buy and platform decisions and ensures every AI investment is tied to a documented business requirement, a named business owner, and a defined measure of success.
DUTIES AND RESPONSIBILITIES
- Develop, maintain, and communicate the enterprise AI roadmap in alignment with the enterprise business systems and AI roadmap owned by the VP, Business Systems & AI Strategy.
- Own the AI use-case portfolio, including intake, assessment, prioritization, and sequencing based on business value, feasibility, data readiness, adoption readiness, risk, and support needs.
- Build business cases with Business Partners and functional leaders, defining baseline metrics, value assumptions, success criteria, and target outcomes before work begins.
- Lead buy-versus-configure-versus-build decisions, recommending whether each use case should be sourced from a specialized 3rd Party Tool, delivered through an existing platform, or built in house.
- Own platform decisions for agentic capabilities within enterprise applications, including the evaluation and use of Salesforce Agentforce and similar embedded agent offerings.
- Lead AI vendor vetting, including market scans, capability assessments, demonstrations, pilots, and proof-of-concepts, in partnership with Procurement, Legal, Cybersecurity, and AI Governance & Operations.
- Manage the portfolio of specialized AI solutions the Company buys, including business ownership, roadmap alignment, performance expectations, service reviews, and renewal recommendations.
- Partner with BPE for process orchestration: map current-state processes, design future-state workflows that combine people, systems, and agents, and define hand-offs, decision points, and human-in-the-loop controls.
- Define agent workflows and requirements, including the scope of autonomy, tools and data each agent may use, escalation paths, and how agent performance will be measured.
- Set and maintain enterprise automation patterns that guide when to use rules-based automation, RPA, workflow tools, AI assistants, or autonomous agents.
- Ensure business and functional requirements are clearly defined, documented, and validated before solution design and implementation.
- Run portfolio reviews with business leaders to decide whether pilots and solutions should be expanded, modified, paused, or retired based on results.
- Partner with AI Governance & Operations to ensure every use case is registered in the AI inventory, risk-tiered, and reviewed through appropriate governance routines.
- Lead and develop AI product managers and AI business analysts, establishing consistent product management practices across the AI portfolio.
- Communicate roadmap status, trade-offs, and portfolio value to the VP, Business Systems & AI Strategy, the Executive Leadership Team, and business stakeholders.
- Fosters the MSC Culture in the department and throughout the company to ensure fulfillment of MSC's vision and unity of purpose.
- Participation in special projects and performs additional duties as required
QUALIFICATIONS
What You Need:
- Bachelor's degree in Business, Information Systems, Engineering, Operations, or related discipline required. Advanced degree preferred.
- A minimum of 10 years of progressive experience in product management, business systems, digital transformation, or technology strategy, including at least 3 years leading product managers or analysts, required.
- Demonstrated experience owning a roadmap or portfolio for AI, automation, or enterprise software products required.
- Working knowledge of generative AI, agents, workflow automation, and responsible AI practices, with enough hands-on fluency to evaluate solutions and challenge vendor claims, required.
- Experience leading vendor evaluations, pilots, and build-versus-buy decisions in partnership with Procurement, Legal, and Security required.
- Experience with business process mapping and redesign.
- Experience with Salesforce, or other major enterprise platforms preferred.
- Experience defining agent success metrics, evaluation criteria, and human-in-the-loop controls preferred.
- Strong business case development, financial acumen, and prioritization skills required.
- Strong facilitation, executive communication, and stakeholder management skills, with the ability to say no and explain why, required.
Bonus Points If You Have:
- Experience leading enterprise AI, automation, or digital transformation portfolios at scale.
- Hands-on experience with agent-based platforms, including Salesforce Agentforce, Microsoft Copilot, or similar technologies.
- Experience implementing AI governance, responsible AI, or AI risk management frameworks.
- Lean, Six Sigma, Product Management, or Agile/Scrum certification.
- Experience designing and optimizing cross-functional business processes and operating models.
- Knowledge of data science, machine learning, analytics, or advanced data platforms.
- Experience presenting AI strategies, investment recommendations, and business outcomes to executive leadership or board.
Other Requirements:
- A valid driver's license may be required.
- Position requires up to 20% of travel
- This position may require access to International Traffic in Arms Regulations Information (“ITAR”) and/or Controlled Unclassified Information (“CUI”)
- INDICATES ESSENTIAL DUTIES
To perform this job successfully an associate must be able to perform each essential duty satisfactorily. The requirements listed below are representative of knowledge, experience level and abilities required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential duties.
Compensation starting at $146,000 - $229,350 depending on candidate location and experience.
The salary range represented is based on similar roles in comparable industries, and the cost of labor in respective cities. Actual compensation is based on the candidate's relevant experience, education requirements and peer pay equity. The Company reserves the right to modify the range as market conditions change.
Applicants must be currently authorized to work in the United States. We are unable to sponsor or take over sponsorship of an employment Visa for this position at this time.
This job posting will remain open for a minimum of 3 business days from the original posting date or longer as needed to fill the position.
WHY MSC?
People. Collaboration. Insight. That's how you build something that works. Built on a foundation of trust, MSC works side by side with our customers to help them drive business results. With more than one million product offerings and 80+ years of experience across industries, MSC strives to help our customers achieve greater productivity, profitability, and growth through inventory management and other innovative supply chain solutions. We care about our associates and have programs in place to help our 6,500+ team members achieve their potential.
OUR COMMITMENT TO YOU
Our associates are our top priority and investing in their well-being is one way we execute our Built to Make You Better brand promise. Alongside competitive pay, we have a comprehensive benefits program to support you and your family's health, well-being and financial future. We offer dynamic healthcare plans, generous 401K and stock purchasing programs, tuition reimbursement opportunities, and paid time away for holidays, vacations, and illness. Visit our page for a better look at our extensive benefits: Your Future Benefits.
You will also have the opportunity to join our Associate Inclusion Circles: Women, Pride, Black, Generational, Veterans, HOLA, and Able. These circles are open to all associates and are designed to promote awareness, collaboration, and respect.
EQUAL EMPLOYMENT OPPORTUNITY STATEMENT
At MSC, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all qualified applicants and our associates without regard to race, color, religion, age, sex, national origin, disability, protected veteran status, sexual orientation or any category protected by applicable law. Accommodation requests can be made at any stage of the recruitment process; applicants are asked to make their needs/requirements known.
Please note that MSC uses electronic monitoring and system logs in its workplace, including monitoring of Company-provided systems and collaboration tools, as permitted by applicable law.
Salary Context
This $146K-$229K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At MSC Industrial Supply Co., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($187K) sits 11% below the category median. Disclosed range: $146K to $229K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
MSC Industrial Supply Co. AI Hiring
MSC Industrial Supply Co. has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Melville, NY, US, Davidson, NC, US. Compensation range: $216K - $229K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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