Data Scientist

Dollar General • Goodlettsville, TN • 21h ago

Work Where You Matter

At Dollar General, our mission is Serving Others! We value each and every one of our employees. Whether you are looking to launch a new career in one of our many convenient Store locations, Distribution Centers, Store Support Center or with our Private Fleet Team, we are proud to provide a wide range of career opportunities. We are not just a retail company; we are a company that values the unique strengths and perspectives that each individual brings. Your difference truly makes a difference at Dollar General. How would you like to Serve? Join the Dollar General Journey and see how your career can thrive.

Company Overview

Dollar General Corporation has been delivering value to shoppers for more than 80 years. Dollar General helps shoppers Save time. Save money. Every day.® by offering products that are frequently used and replenished, such as food, snacks, health and beauty aids, cleaning supplies, basic apparel, housewares and seasonal items at everyday low prices in convenient neighborhood locations. Learn more about Dollar General at www.dollargeneral.com/about-us.html .

Job Details

This key role within Decision Science & Analytics will lead the ongoing development and execution of the Company's product shrink and damages forecasting and mitigation efforts. This role will work collaboratively with internal business partners (such as Asset Protection, Finance, and Merchandising), internal IT resources, and the broader Decision Science organization. Day to day work will include developing and monitoring predictive and deterministic models, automating analytics processes, and deep dive ad hoc analysis to determine root causes

Duties & Responsibilities:

Develop dynamic, productionized, and scalable models that generate ROI for both DG and their customers. These models may include predictive and time-series forecasts, store segmentations, or include geospatial components.

Create automated, reusable analytics workflows from end-to-end: from developing and maintaining SQL/Python code to the final report or dashboard deliverable

Conduct open ended ad hoc analysis, including understanding the key business problem/question, translating needs into the appropriate analytics techniques, and delivering insights and recommendations to drive decisions

Present analytical findings and actionable insights in PowerPoint or PowerBI to stakeholders, "telling the story" with data to non-technical audiences

Knowledge, Skills and Abilities (KSAs): What KSAs are required to perform this job?

Strong problem-solving skills utilizing expertise, business judgment and robust quantitative analyses

Experience developing predictive models from scratch and moving into production environment, including model comparison/selection techniques, parameterization, error handling, and working across dev/test/prod environments

Identify and implement proper data preparation and feature engineering methods, such as outlier identification and removal, principal components analysis (PCA), and general data structuring

Experience developing code to combine, clean and prepare data for modeling using some combination of SQL, Python, and PySpark (including but not limited to pandas, numpy, scikit-Learn, matplotlib, tensor-flow)

Proficiency with common analytical platforms, including distributed compute, such as Databricks

Practical experience ingesting and manipulating large volumes of data (millions of records)

Demonstrated ability to translate complicated analytics topics and insights into easily communicable concepts to non-technical audience, including model accuracy and feature importance

Experience with geospatial analytics preferred

Experience with code management tools such as GitLab

Experience with retail industry preferred

Basic to intermediate skills with BI tools such as Tableau and PowerBI preferred

Qualifications

Work Experience &/or Education:

MS in Data Science, Statistics, Economics, Computer Science, Mathematics, or related applied quantitative field preferred. Bachelor's in a highly quantitative/STEM field considered with the right experience.

2-5 years hands-on industry (non-academic) experience in Data Science (or equivalent quantitative job title). Strong background in applying statistical machine learning techniques to predictive modeling and experience with Machine Learning libraries.

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