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Data Scientist

Grainger Businesses

Chicago, Illinois, United StatesOn Site

Original Job Summary

About Grainger

W.W. Grainger, Inc. is a leading broad line distributor operating primarily in North America, Japan, and the United Kingdom. The company serves over 4.5 million customers worldwide, delivering products and solutions through innovative technology and deep customer relationships. Grainger reported a 2024 revenue of $17.2 billion across its two business models.

For more details, visit the official website.

Compensation & Benefits

The anticipated base pay range is $79,000.00 to $131,600.00. Benefits start on day one and include medical, dental, vision, and life insurance, 18 PTO days plus company holidays, a 401(k) with 6% company contribution, and numerous other employee programs.

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Position Details

The Data Scientist is responsible for building and maintaining large-scale statistical models that transform data into actionable insights. This hybrid role requires working onsite in Lake Forest or Chicago, IL three days a week.

  • Propose and evaluate innovative solutions for data analysis.
  • Develop, validate algorithms and write production code.
  • Collaborate with QA and cross-functional teams on product development.
  • Translate complex data analysis into clear insights.
  • Contribute to research publications and training sessions.

Key Skills/Competency

Data Mining, Statistical Modeling, SQL, Python, R, Machine Learning, Algorithms, Data Visualization, Regression, Distributed Systems

How to Get Hired at Grainger Businesses

🎯 Tips for Getting Hired

  • Customize your resume: Highlight relevant data science and algorithm skills.
  • Research Grainger: Understand their business and culture.
  • Prepare examples: Detail successful model deployments and projects.
  • Practice technical interviews: Focus on SQL, Python, and statistics.

📝 Interview Preparation Advice

Technical Preparation

Review SQL and database management.
Practice Python/R coding exercises.
Study statistical modeling and regression.
Revisit algorithm design and data visualization.

Behavioral Questions

Describe a collaborative project experience.
Explain handling tight deadlines effectively.
Discuss conflict resolution in team settings.
Share examples of problem-solving in analytics.