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About This Role
GALLS, LLC is the largest and fastest growing supplier of uniforms and equipment to public safety professionals, with a national presence in more than 80 locations. With over 50 years in the industry, it is easy to see why. We are PROUD to serve America’s public safety professionals by providing the broadest selection of uniforms, equipment, and solutions combined with great customer service.
As an ecommerce\-driven business operating in a fast\-moving, always\-on environment, we are seeking a highly technical and commercially minded AI/ML Data Scientist to build intelligent agents, automation systems, and scalable workflows that can continuously monitor, analyze, and execute business processes at scale. The objective is to develop intelligent, data\-driven capabilities that enable the business to operate more efficiently, proactively, and intelligently 24/7\.
WHAT YOU'LL DO
This role combines hands\-on AI/ML model development, data engineering, AI architecture, analytics, and strategic business problem\-solving. The ideal candidate will design and implement intelligent systems that improve operational efficiency, automate workflows, optimize pricing and merchandising, enhance financial analysis, and support business growth initiatives.
You will work closely with cross\-functional teams across Ecommerce B2C/B2B cycles to build practical AI solutions leveraging modern machine learning, LLMs, Retrieval Augmented Generation (RAG), agentic AI systems, and advanced analytics frameworks.
This role offers the opportunity to take ownership of enterprise AI initiatives while building scalable systems that directly impact business performance.
Key Responsibilities
AI Strategy \& Intelligent Automation
- Lead the development and execution of scalable AI and automation initiatives across the business
- Identify opportunities to improve operational efficiency, decision\-making, and business performance through intelligent, data\-driven solutions
- Design and implement systems leveraging LLMs, RAG, agentic AI frameworks, knowledge graphs, and advanced machine learning techniques
- Build AI agents and autonomous workflows capable of supporting a 24/7 ecommerce operation
- Research, evaluate, and implement emerging AI technologies, frameworks, and methodologies
AI/ML Modeling \& Solution Development
- Develop, train, validate, and deploy machine learning, NLP, and GenAI models for operational and commercial use cases
- Apply machine learning techniques including forecasting, optimization, recommendation systems, anomaly detection, and predictive analytics
- Perform feature engineering, experimentation, model evaluation, benchmarking, and performance optimization
- Support the continuous improvement, scalability, and reliability of AI/ML systems and analytics solutions
- Establish evaluation and monitoring frameworks for AI and GenAI model performance
Data Engineering \& Enterprise Analytics
- Build and maintain scalable data pipelines, ingestion systems, and automation workflows
- Develop scripts and processes for data scraping, collection, cleansing, normalization, and enrichment
- Integrate and centralize data from APIs, ecommerce platforms, ERP systems, databases, and third\-party providers
- Ensure enterprise data is reliable, accessible, and structured for analytics, reporting, and intelligent automation initiatives
- Deliver analytics, dashboards, forecasting models, and reporting solutions supporting pricing, finance, merchandising, operations, inventory, and growth initiatives
Cross\-Functional Collaboration
- Partner with stakeholders across Ecommerce, Operations, Finance, Compliance, Legal, Merchandising, Risk, and IT to deliver scalable AI and analytics solutions
- Translate business challenges into structured AI, machine learning, and data science initiatives
- Support the adoption and integration of AI\-driven tools, workflows, and automation capabilities across the organization
- Communicate technical findings, insights, and recommendations clearly to both technical and non\-technical stakeholders
Technical Leadership \& Best Practices
- Contribute to the design and evolution of scalable AI, data, and analytics architectures
- Establish best practices for model development, deployment, governance, experimentation, and monitoring
- Ensure adherence to SDLC standards, documentation, version control, and software engineering best practices
- Collaborate with Data Engineering and ML Engineering teams to support production deployment and operational scalability
- Stay current with advancements in AI, machine learning, data engineering, and intelligent automation technologies
WHAT YOU BRING
- Master’s or PhD degree in Computer Science, Machine Learning, Engineering, or another highly quantitative discipline
- 5\+ years of hands\-on experience building AI/ML/NLP solutions and applying statistical analysis to solve complex business problems
- Strong programming skills in Python and SQL, with experience developing scalable production\-ready solutions
- Experience designing and deploying systems leveraging LLMs, RAG pipelines, agentic AI frameworks, vector databases, and semantic search
- Experience with modern AI/ML frameworks and tooling such as LangChain, LangGraph, CrewAI, OpenAI SDK, Hugging Face, PyTorch, and related ecosystems
- Experience working with data pipelines, APIs, ETL workflows, and cloud\-based data platforms
- Familiarity with vector stores, graph databases, SPARQL, Linux environments, and modern software engineering practices
- Experience with experimentation, benchmarking, model evaluation, and LLM performance assessment methodologies
- Strong understanding of SDLC principles, Git/version control workflows, and scalable software architecture
- Experience supporting ecommerce, B2B/B2C operations, merchandising, pricing, finance, fraud/risk, or operational analytics initiatives
- Strong analytical, communication, problem\-solving, and stakeholder management skills
WHAT YOU SEND OUR WAY
- Your resume, highlighting your education, experience, and skills
WHAT WE OFFER
- Excellent medical/dental and vision coverage—Eligible 1st day of the month after start date
- 401(k) retirement plan with company contribution (because you will retire someday)
- Flexible benefits—choose what you like, ignore the rest
- Generous employee discount
- Vacation and Personal Time
- Paid Holidays
- Tuition reimbursement
- Daily Pay: up to 50% of your pay
EOE/Disability/Veterans
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 Galls, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Galls AI Hiring
Galls has 1 open AI role right now. They're hiring across Data Scientist. Based in 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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