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About This Role
Our Mission
Our mission is to keep the workforce safe on the job by providing comfortable, high\-quality protective footwear—helping reduce accidents and save money.
About Shoes For Crews, LLC (SFC)
Shoes For Crews is a dynamic designer, manufacturer, and B2B marketer of technologically advanced, top\-rated slip\-resistant footwear. We serve the foodservice, hospitality, industrial, and healthcare industries, protecting over 3\.5 million workers annually across more than 120,000 workplaces. For over 30 years, we’ve built our reputation on innovation, proprietary manufacturing, and targeted niche marketing.
Position Summary
We are seeking a Senior Data \& Business Intelligence Analyst (AI \& SQL) who combines deep technical expertise with strong business acumen.
This role is ideal for someone who:
- Understands business data and can translate it into actionable insights
- Supports Sales and cross\-functional teams with data\-driven answers
- Writes advanced SQL (including stored procedures)
- Leverages AI tools to enhance reporting and analytics
- Has experience building reports with and administering Power BI and Tableau
You will act as a key data partner to the business, delivering insights that drive decision\-making while ensuring data accuracy, accessibility, and usability.
Key Responsibilities
- Design, build, and maintain business intelligence and analytics solutions
- Write and optimize SQL queries and stored procedures for reporting and data transformation
- Develop and deliver AI\-assisted reports and insights to support business needs
- Create dashboards and reports using Power BI, Excel, and Tableau
- Translate business questions into clear, actionable data insights for Sales and other teams
- Develop scalable data models from multiple data sources
- Lead enhancements and migration of reporting (e.g., Tableau to Power BI)
- Ensure data quality, governance, and consistency across platforms
- Partner with business stakeholders to understand needs and provide data\-driven recommendations
- Document data definitions, reporting logic, and BI best practices
Qualifications \& Skills
Technical Expertise
- Advanced SQL skills, including stored procedures and data optimization
- Experience with:
+ SQL Server, SSRS, SSIS
+ Microsoft Power BI / Power Platform
+ Tableau
- Strong knowledge of data warehousing, ETL processes, and data modeling
- Experience or exposure to AI tools for reporting and analytics
Business \& Analytical Skills
- Strong understanding of business metrics, including:
+ Sales performance
+ Revenue and expenses
+ Forecasting and trends
- Ability to translate complex data into clear insights for non\-technical users
- Experience supporting Sales or operational teams with data requests
- Strong problem\-solving and analytical thinking
Experience
- Proven experience as a BI Analyst, Data Analyst, or BI Developer
- Degree or equivalent experience in Data Analytics, Computer Science, Information Systems, or related field
- Ability to work effectively across cross\-functional teams and stakeholders
What Makes You Stand Out
- Ability to bridge the gap between technical data and business needs
- Technical expertise with SQL, Power BI, and Tableau
- Strong communication skills—you can explain data to any audience
- Proactive mindset with a focus on delivering insights, not just reports
- Interest in applying AI to improve analytics and reporting efficiency
Candidates located in the Boca Raton area will be given preference for this hybrid role; however, we are open to remote candidates for the right fit.
Applicants must be authorized to work in the U.S. without current or future employer sponsorship.
*Shoes For Crews is proud to be an equal opportunity employer committed to hiring a diverse and inclusive workforce. Shoes For Crews provides equal employment opportunities to all employees and employment applicants without regard to unlawful considerations of sex, sexual orientation, gender (including gender identity and/or expression), pregnancy, race, color, creed, national or ethnic origin, citizenship status, religion, disability, marital status, age, genetic information, veteran status or any personal attribute or characteristic that is protected by applicable local, state, or federal laws.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Shoes for Crews North America, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Shoes for Crews North America AI Hiring
Shoes for Crews North America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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