Data Analyst, Cross Reference

Global Industrial

For over 70 years Global Industrial has been an industry leader providing private label and brand name industrial equipment and supplies to businesses throughout North America.

We carry over one million industrial, material handling and business products that are sold through our website, corporate sales people and full color catalogs. We are constantly increasing our product offerings to meet the diverse and changing needs of our customers. Our customers include small to large corporations, institutions, government agencies and consumers across North America.

Key Responsibilities

Cross Reference Tool Development
Enhance and maintain the existing Python-based cross reference tool used to identify equivalent products across multiple manufacturers.
Improve product matching algorithms to increase cross-reference accuracy and coverage.
Develop improved match grading models that provide clearer confidence levels for product equivalency.
Data Infrastructure & Master Data Management
Develop and maintain a centralized master product data table integrating manufacturer, vendor, and internal catalog data.
Clean and structure large product datasets to improve usability and eliminate outdated or unnecessary data elements.
Capture and incorporate cross-reference results from daily operations into the master data model to continuously improve matching intelligence.
Analytics & Model Development
Develop statistical and machine learning models that improve product matching, substitution recommendations, and pricing analysis.
Build automated workflows that support bid and proposal analytics.
Evaluate model performance and continuously refine algorithms to improve results.
Data Visualization & Reporting
Support and enhance existing Power BI dashboards (XACT and XACT for Sales) used by internal teams.
Develop new dashboards that provide insights into cross-reference performance, match rates, and opportunity tracking.
Automation & Process Improvement
Identify opportunities to automate repetitive analytics processes currently dependent on manual data preparation.
Develop scalable analytics tools that reduce reliance on other teams for tool maintenance and updates.
Cross-Functional Collaboration
Partner with Pricing, Contracts, Product Management, and Cross Reference teams to identify opportunities for data-driven improvements.
Translate operational challenges into analytical solutions that improve business outcomes.

Competencies and skills

Core Competencies
Advanced analytical and problem-solving skills
Strong understanding of statistical modeling and machine learning concepts
Ability to translate complex data insights into practical business tools
Data quality management and data governance awareness
Ability to work cross-functionally with both technical and business teams
Strong communication and data storytelling capabilities
Technical Skills
Programming
Python (primary)
SQL
Data Visualization
Power BI
Data Platforms
SQL Server or similar relational databases
Large dataset processing and optimization
Development Tools
Jupyter Notebook

Qualifications:
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field
3-5+ years experience working with large datasets and building data-driven tools
Experience building analytics tools in Python
Experience working with product data, pricing analytics, or supply chain data is a plus

EEO/AA Statement

Global Industrial provides equal employment opportunities to all employee and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absences, compensation and training.