Job Opportunity: Director of Analytics & Machine Learning - Fulfillment Operations

Location: Onsite

Grainger is a leading industrial distributor with operations across North America, Japan, and the United Kingdom. We Keep The World Working® by serving over 4.5 million customers globally, delivering products through cutting-edge technology and exceptional customer relationships. In 2023, our sales reached $16.5 billion, showcasing our unwavering commitment to customer value, team engagement, and robust financial performance.

Join our award-winning workplace, recognized as a 2024 Glassdoor Best Place to Work and a Great Place to Work-Certified™ company. Be part of our passion-driven team and make a meaningful impact as we continue our century-long industry leadership.

Position Overview

As the Director of Analytics & Machine Learning at Grainger, you will be integral to our goal of delivering 30M+ shipments to 4.5M customers from an expansive network of 250+ distribution centers and branches. Our Operations Support team ensures the efficiency of this network by analyzing operational health, driving performance improvements, and optimizing cost-effectiveness.

Role Responsibilities:

• Lead and mentor a team of business analysts, data scientists, and data engineers to solve business challenges and guide data-driven decision-making.
• Develop and implement machine learning capabilities to analyze large datasets, uncover patterns, and derive actionable insights.
• Collaborate with various business units to define key performance indicators (KPIs) and metrics, and measure the success of operations and initiatives.
• Create and manage analytical dashboards, tools, and applications that facilitate cross-functional collaboration and decision-making.
• Design, develop, and deploy machine learning models and algorithms to address complex business problems.
• Oversee the lifecycle of analytics and machine learning projects, from problem definition to data collection, model deployment, and monitoring.
• Communicate analytics findings, progress, and recommendations to senior leadership.
• Identify opportunities for process optimization, automation, and scalability in analytics workflows.

Qualifications:

• Bachelor’s degree in statistics, economics, mathematics, or a related field. Master’s degree preferred.
• 10+ years of relevant work experience.
• Proven experience leading analytics teams and delivering actionable business insights.
• Expertise in machine learning capabilities and techniques, including linear regression, logistic regression, cluster analysis, and time-series analysis.
• Proficiency with SPSS, SAS, R, Python, and SQL.
• Exceptional ability to handle high-exposure responsibilities and interact with senior leadership.
• Strong business acumen and strategic thinking.
• Excellent communication skills and the ability to establish and maintain robust business relationships.

Note: Occasional travel may be required to DC and branch field locations.

Rewards and Benefits:

Enjoy comprehensive benefits starting on day one. Our flexible programs are designed to meet the diverse needs of our team members. Visit for a detailed overview. Key benefits include:

• Medical, dental, vision, life, and pet insurance plans, plus 6 free therapy sessions annually.
• Paid time off (PTO) and 6 company holidays per year.
• 6% company contribution to a 401(k) Retirement Savings Plan each pay period, no match required.
• Employee discounts, tuition reimbursement, student loan refinancing, and free financial counseling.
• Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and 4 weeks for non-birth parents.

Grainger is committed to equal employment opportunities, fostering an inclusive workplace regardless of race, color, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or veteran status.

If you need reasonable accommodation during the application and selection process, please let us know so we can provide the necessary assistance.

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