About the role

Data Scientist leveraging machine learning and statistical analysis for business insights at Grainger. Driving value and growth through data-driven decisions and innovative solutions.

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 customer 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.

Bachelor's Degree Data Science, Statistics, Mathematics, Computer Science, or other quantitative field.
Master’s Degree Data Science, Statistics, Mathematics, Computer Science, or other quantitative field (Preferred).
2+ years experience in specialized research, such as informatics, data collection, scientific research and research equipment (Required).
Proficiency in databases such as Teradata, Snowflake or Oracle and querying languages (e.g. SQL) (Experienced).
Experienced with at least one data science programming languages (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.

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.