Data Scientist

Work Location Type: Hybrid

About Grainger

W.W. Grainger, Inc. is a leading broad line distributor with operations primarily in North America and Japan. At Grainger, We Keep the World Working® by serving more than 4.6 million customers worldwide with maintenance, repair and operating (MRO) products and value-added solutions delivered through innovative technology and deep customer expertise. Known for its commitment to service and purpose-driven culture, the Company reported 2025 revenue of $17.9 billion.

Pay

The anticipated base pay compensation range for this position is $87,200.00 – $145,300.00. This role is eligible for an incentive target of up to 5%, based on the achievement of individual and company performance objectives in accordance with the current terms of the incentive program which are subject to change.

Benefits

With benefits starting on day one, our programs provide choice and flexibility to meet team members' individual needs, including:

• Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing.
• 18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year.
• 6% company contribution to a 401(k) Retirement Savings Plan each pay period, no employee contribution required.
• Employee discounts, tuition reimbursement, student loan refinancing and free access to financial counseling, education, and tools.
• Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents.

Responsibilities

• Work closely with the business to understand the problem space, identify the opportunities, and translate business problems into technical solutions using machine learning frameworks.
• Conduct exploratory data analysis and apply deep business knowledge to customers and marketplace data to uncover new business insights.
• Manipulate high-volume, high-dimensionality data from multiple sources, visualize patterns, anomalies, relationships, and trends, and perform feature engineering and selection.
• Design and conduct experiments, collect the data necessary to perform statistical hypothesis testing, and create inferences and recommendations.
• Create scalable, efficient, automated processes to support large scale data analyses, model development, model validation and deployment.
• Apply techniques such as classification, clustering, dimension reduction, regression, NLP, deep learning, time series forecasting to build explanatory, predictive, prescriptive models appropriate for solving different business problems.
• Create and present the materials necessary to effectively communicate the results of analytical work and associated recommendations.
• Develop expertise on Grainger's business operations, go-to-market model, and the broader Maintenance, Repair, and Operations (MRO) market.

Requirements

• Bachelor’s Degree in Data Science, Statistics, Mathematics, Computer Science, or other quantitative field and/or Master’s Degree in Data Science, Statistics, Mathematics, Computer Science, or other quantitative field.
• Minimum 2+ years’ experience in Data science or statistics-related work experience.
• Proficiency in databases such as Snowflake, Teradata, or Oracle and querying languages (e.g. SQL).
• Experience with at least one data science programming language (e.g., Python, R) and working with structured and unstructured datasets.
• Experience with machine learning techniques such as classification, clustering, dimension reduction, regression, NLP algorithms, and time series modeling.
• Experience with statistical design of experiments, outlier detection methods, and statistical hypothesis testing.
• Experience with data visualization techniques and tools.
• Experience translating work into presentations (e.g. PowerPoint) suitable for non-technical audience.
• Demonstrated ability to collaborate with business partners and colleagues.