Data Scientist

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., * 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 audience.

• 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.