Interested in this Data Scientist role at Galls?
Apply Now →Skills & Technologies
About This Role
JOB SUMMARY
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 AI transforms digital commerce, pricing is evolving from a manual, rules\-based function into an intelligent, autonomous capability. The AI Commerce Pricing Strategist will lead the development, governance, and optimization of AI\-powered pricing strategies that maximize revenue, profitability, and competitive positioning across our ecommerce portfolio.
This role is responsible for overseeing algorithmic pricing models, defining business guardrails for autonomous pricing agents, and ensuring our pricing strategy is optimized for both traditional ecommerce channels and emerging AI\-powered shopping experiences (e.g., ChatGPT, Gemini, Perplexity, and autonomous shopping agents). Working at the intersection of Ecommerce, Data Science, Merchandising, Finance, and Marketing, this individual will transform pricing into a strategic growth lever that improves customer acquisition, conversion, margin, and long\-term profitability.
WHAT YOU’LL DO
AI Pricing Strategy \& Governance
- Develop and govern AI\-driven pricing strategies across ecommerce channels.
- Define pricing guardrails, margin thresholds, competitive rules, and business constraints for autonomous pricing engines.
- Continuously monitor and optimize pricing algorithms to balance revenue growth, profitability, and customer value.
- Evaluate and implement emerging AI pricing technologies and automation platforms.
Dynamic Pricing \& Revenue Optimization
- Build dynamic pricing strategies using demand signals, inventory position, seasonality, competitive pricing, and customer behavior.
- Develop pricing frameworks that maximize gross margin while remaining competitive in the market.
- Optimize pricing across product lifecycle stages, including launches, replenishment, promotions, and clearance.
Agentic Commerce \& AI Search Optimization
- Ensure pricing, product catalogs, and structured data are optimized for AI\-powered search engines and autonomous shopping agents.
- Partner with SEO, Product Content, and Digital teams to improve AI discoverability and product recommendation accuracy.
- Monitor AI\-generated pricing and product recommendations to strengthen competitive positioning.
Competitive Intelligence
- Leverage AI\-powered competitive intelligence platforms to monitor market pricing, promotional activity, assortment changes, and pricing elasticity.
- Identify pricing opportunities, competitive threats, and emerging market trends.
- Develop predictive pricing recommendations based on competitive simulations and demand forecasting.
Promotion \& Markdown Optimization
- Partner with Merchandising and Inventory Planning to optimize promotional calendars and markdown strategies.
- Utilize machine learning models to determine optimal timing, depth, and duration of promotions while protecting margin and brand equity.
- Measure promotional effectiveness and continuously refine pricing strategies based on performance.
Data \& Business Analytics
- Develop dashboards and executive reporting for key pricing and ecommerce KPIs including:
- Revenue
- Gross Margin
- Contribution Margin
- Customer Acquisition Cost (CAC)
- Average Order Value (AOV)
- Conversion Rate (CVR)
- Price Elasticity
- Promotional ROI
- Competitive Price Index
- Translate complex pricing analytics into actionable business recommendations for senior leadership.
Cross\-Functional Leadership
- Partner closely with Ecommerce, Merchandising, Finance, Marketing, Supply Chain, Product Management, and Data Science teams.
- Translate business objectives into machine\-readable pricing rules and AI model requirements.
- Champion AI adoption across pricing and revenue management processes.
WHAT YOU BRING
- 5\+ years of experience in Ecommerce Pricing, Revenue Management, Digital Merchandising, Pricing Strategy, or Advanced Analytics.
- Experience working with AI\-powered pricing optimization platforms or dynamic pricing technologies.
- Experience building pricing strategies that balance growth, profitability, and customer experience.
Technical Skills
- Strong understanding of AI, Machine Learning, predictive analytics, and pricing optimization concepts.
- Experience with pricing analytics, SQL, Python, or business intelligence platforms such as Tableau, Looker, or Power BI.
- Familiarity with product feeds, structured data, ecommerce platforms, and AI search optimization.
Preferred Qualifications
- Experience with LLM optimization, Answer Engine Optimization (AEO), or AI commerce ecosystems.
- Knowledge of Model Context Protocol (MCP), product knowledge graphs, and structured commerce data.
- Experience integrating competitive intelligence and pricing automation platforms.
WHY THIS ROLE MATTERS
As AI reshapes how customers discover, evaluate, and purchase products, pricing will become increasingly autonomous and data\-driven. This role will ensure our pricing strategy evolves with the market, enabling us to compete effectively in both traditional ecommerce and emerging AI\-powered commerce while driving profitable, sustainable growth.
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/Vets
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 4,317 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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 $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.