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About This Role
Date: Jul 16, 2026
Location: LAKE FOREST, IL, US, 60045\-5202 CHICAGO, IL, US, 60661\-4555
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Company: Grainger Businesses
Work Location Type: Hybrid
Req Number 332078
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. For more information, visit www.grainger.com.
Compensation
The anticipated base pay compensation range for this position is $103,600\.00 – $172,600\.00\. This role is eligible for an incentive target of up to 10 %, 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.
Rewards and 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.
For additional information and details regarding Grainger’s benefits, please click on the link below:
https://experience100\.ehr.com/grainger/Home/Tools\-Resources/Key\-Resources/New\-Hire
Grainger Benefits
The pay range provided above is not a guarantee of compensation. The range reflects the potential base pay for this role at the time of this posting based on the job grade for this position. Individual base pay compensation will depend, in part, on factors such as geographic work location and relevant experience and skills.
The anticipated compensation range described above is subject to change and the compensation ultimately paid may be higher or lower than the range described above.
Grainger reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion at any time, consistent with applicable law.
Position Details
We are looking for a Sr Data Scientist to join our Inventory Optimization team, bringing expertise in data analysis and advanced quantitative methods (machine learning and operations research) to drive impactful business outcomes. These analytics\-based solutions are used to improve how Grainger’s supply chain is designed, operated and maintained, aligning solutions with strategic goals and service to our customers. The ideal candidate is intellectually curious, continuously learning, and motivated to explore new technologies, business insights, and customer behaviors. This role requires a strong commitment to innovation and long\-term value creation through data\-driven decision\-making.
This position is to be located hybrid (2 days per week) from either our Lake Forest, IL or downtown Chicago office is preferred. Remote/virtual may be considered for candidates outside this area.
You Will
Analyze data sets, build predictive models, leverage operations research, and deploy and operationalize solutions to enhance supply chain performance.
Apply operations research techniques such as linear programming, integer programming etc. and machine learning techniques such as classification, clustering, regression, and time series forecasting to build explanatory, predictive, and prescriptive models appropriate for solving different supply chain problems.
Conduct exploratory data analysis and apply supply chain understanding to uncover new insights and form recommendations rooted in analytics.
Create and present the materials necessary to communicate the results of analytical work and associated recommendations and influence the use of analytical recommendations.
Use your experience on Grainger’s business operations, go\-to\-market model, and the broader Maintenance, Repair, and Operations (MRO) market to inform analytical decisions and recommendations.
Manipulate high\-volume, high\-dimensionality data from multiple sources, visualize patterns, anomalies, relationships, and trends, and perform feature engineering and selection.
Create the code to support large\-scale data analyses, model development, model validation and deployment.
Assist junior team members in developing new skills and knowledge.
You Have
Bachelor's Degree BS in technical field such as Statistics, Mathematics, Data Science, Applied Analytics, Operations Research, Applied Science or Engineering required
Advanced degree (Master's Degree MS or PhD) in technical field such as Statistics, Mathematics, Data Science, Applied Analytics, Operations Research, Applied Science or Engineering preferred
3\+ years experience in analytics and data science roles required
Proficient in usage of databases (e.g. Teradata, Snowflake, Oracle) and querying languages (e.g. SQL)
Knowledge of one or more of the following programming languages: Python, R, SPSS, or SAS
Proficiency with extraction and manipulation of very large structured and unstructured datasets
Proficiency with data visualization techniques
Proficiency with multi\-variate linear regression, logistic regression, and time series modeling
Proficiency with statistical design of experiments, outlier detection methods, and statistical hypothesis testing
Proficiency with clustering and dimension reduction techniques
Knowledge of classification, gradient\-boosting, and natural language processing algorithms
Experience leveraging cloud\-based machine learning resources such as those from AWS
Demonstrated ability to translate analytical work into presentations (e.g. PowerPoint) suitable for non\-technical audiences
Demonstrated ability to collaborate with business partners and colleagues
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, protected veteran status or any other protected characteristic under federal, state, or local law. We are proud to be an equal opportunity workplace.
We are committed to fostering an inclusive, accessible work environment that includes both providing reasonable accommodations to individuals with disabilities during the application and hiring process as well as throughout the course of one’s employment, should you need a reasonable accommodation during the application and selection process, including, but not limited to use of our website, any part of the application, interview or hiring process, please advise us so that we can provide appropriate assistance.
Salary Context
This $103K-$172K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Grainger, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($138K) sits 28% below the category median. Disclosed range: $103K to $172K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Grainger AI Hiring
Grainger has 1 open AI role right now. They're hiring across Data Scientist. Based in Lake Forest, IL, US. Compensation range: $172K - $172K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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