Sr Data Scientist, Promo Model Development

Key Skills

Apache Hadoop, Continuous Integration & Continuous Delivery - CI/CD, Databricks, Decision trees, Large data sets, Logistic Regression, Machine learning techniques, matplotlib, Natural Language Processing (NLP), Neural Networks, NumPy, Pandas, PowerBI, Predictive analytics, Predictive Modeling, PySpark, Python Programming, Random forests, Scikit-learn, Snowflake, SQL, Statistical modeling, SVN, Tableau, TensorFlow

Job Description

As a Senior Data Scientist -Promotions Optimization with Dollar General you'll develop models and algorithms for our retail business. You'll work closely with Marketing and Merchandising teams to continuously learn more about our business and develop an understanding of the various business processes. You will partner with ML Engineers within our IT team and Data Scientists on our Decision Science teams to build solutions for business needs including development, deployment, and maintenance of algorithms and models at scale!
You will oversee the creation and development of key models which allow us to evaluate and guide promotional planning for product promotion.

Duties & Responsibilities:

• Become a domain expert in promotional data at Dollar General. Execute solutions to business problems using data analysis, data mining, forecasting, optimization tools, machine learning techniques, and statistics
• Write code for math/stats models to evaluate demand for all promoted items. Partner with business users and other scientists / engineers to build a framework allowing us to develop a new platform to estimate promotional sales
• Synthesize analysis and results in compelling storylines and communicate to key stakeholders. You should have the ability to tell a story around the problem, the solution and the impact and value of implementing a solution
• Coordinate the analysis, troubleshooting, and resolution of issues in our software and the broader infrastructure. Serve as an advocate of our data-driven culture with Product, Business, and Engineering to help them utilize insights that'll direct their efforts towards initiatives with highest impacts

Qualifications

Knowledge, Skills and Abilities (KSAs):

• Strong problem-solving skills utilizing expertise, business judgment and robust quantitative analyses
• Develop 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)
• 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 with common modeling techniques, such as logistic regression, decision trees, random forest, SVM, regularized regression, neural networks, and natural language processing (NLP)
• Demonstrated ability to translate complicated analytics topics into easily communicable concepts to less technical audience, including model accuracy and feature importance
• Practical experience ingesting and manipulating large volumes of data (millions of records)
• Proficiency with common analytical platforms, including distributed compute (e.g. Databricks, Hadoop, Snowflake, etc.) and Tableau/PowerBI
• Experience with code management tools such as Github (familiarity with CI/CD practices preferred)
• Experience with retail industry or marketing and media networks is preferred

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