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About This Role
Staples is business to business. You're what binds us together.
Our world-class sales and sales support teams work directly with businesses of all sizes to deliver products, services, and solutions that meet our customers' unique needs. We are committed to understanding our customers and leveraging best-in-class sales tools and technology to drive results. We continuously seek new ways to innovate, grow our skills, and invest in career development, creating opportunities to advance both within and beyond sales.
The Manager, Sales Intelligence & Strategy will own the evolution of a high-impact sales intelligence platform that helps Staples sellers prioritize customers, uncover opportunities, and take action. This role sits at the intersection of product ownership, analytics, and AI enablement, partnering closely with Sales, Salesforce, Data, and Strategy teams to enhance how sales insights are delivered and consumed.
Approximately half of the role is hands-on, focused on developing analytics solutions, semantic models, and AI-enabled capabilities that improve seller productivity and business outcomes. The other half is dedicated to product ownership, stakeholder partnership, roadmap planning, and driving adoption of new capabilities. This is a unique opportunity to shape the future of AI-powered sales intelligence at Staples while building scalable solutions that directly impact seller effectiveness and business performance.
What you'll be doing:
- Own the design, development, operation, and continuous improvement of strategic sales intelligence products and solutions.
- Translate business strategies into requirements, priorities, metrics, business rules, and data model designs.
- Analyze sales performance to identify opportunities, risks, root causes, and recommended actions.
- Build or guide scalable analytics solutions, dashboards, semantic models, and automated reporting capabilities.
- Establish validation, documentation, monitoring, and change controls to ensure solutions remain accurate, stable, and sustainable.
- Manage product roadmaps and multiple initiatives while balancing immediate business needs with long-term scalability.
- Drive implementation, adoption, feedback collection, and continuous enhancement of new and existing capabilities.
- Contribute directly to analysis and solution development while providing leadership across major initiatives.
- Coach and mentor analysts, data scientists, developers, and project contributors.
- Partner with Sales, Strategy, Operations, Data, and Technology teams to align business needs with technical solutions.
- Present complex insights, recommendations, and requirements to technical, business, and executive audiences.
- Evaluate and apply emerging analytical methods, automation technologies, and AI capabilities that create meaningful business value.
What you bring to the table:
- Strong strategic, analytical, and problem-solving skills.
- Hands-on technical aptitude with a disciplined approach to quality and detail.
- Excellent leadership, project ownership, and stakeholder management capabilities.
- Strong written, verbal, and presentation skills.
- The ability to translate complex data into clear business recommendations.
- A passion for continuous improvement, innovation, and delivering measurable business impact.
What's needed - Basic Qualifications:
- 7+ years of experience in analytics, business intelligence, data science, sales strategy, revenue operations, or a related field.
- 3+ years of experience leading people, major projects, or cross-functional initiatives.
- Experience building or owning analytical, reporting, automation, or decision-support solutions.
- Proficiency with analytics and data platforms such as Tableau, Alteryx, Salesforce, Snowflake, or similar tools.
- Knowledge of data modeling, semantic layers, metric governance, and business intelligence best practices.
- Experience querying, preparing, analyzing, and interpreting complex data using SQL or other analytical methods.
- Experience leading complex initiatives from requirements gathering through implementation and adoption.
- Strong quantitative, communication, interpersonal, and organizational skills.
What's needed - Preferred Qualifications:
- Bachelor's degree in Analytics, Data Science, Statistics, Computer Science, Business, or a related field.
- Advanced degree in a related discipline.
- Experience developing or maintaining enterprise semantic models and governed analytics solutions.
- Experience leading enterprise-scale or highly cross-functional initiatives.
- Demonstrated success implementing data-driven solutions that delivered measurable business outcomes.
We Offer:
- Inclusive culture with associate-led Business Resource Groups
- 22 days of PTO and Holiday Schedule (7 observed paid holidays + 1 floating holiday)
- Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more!
The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced by a variety of factors including the candidate's experience, skill set, education, geography, business considerations, and internal equity. In addition to base pay, this role may be eligible for bonuses or other forms of variable compensation.
Salary Context
This $118K-$162K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Staples, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $118K to $162K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Staples AI Hiring
Staples has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Framingham, MA, US, Remote, US, Framingham, MA. Compensation range: $162K - $229K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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