Lead Data Scientist- Model development

Key Skills

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

Job Description

As a Lead Data Scientist-Model development 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 oversee the creation and development of key models which allow us to evaluate and guide Dollar General’s customer and marketing analytics program.

Duties & Responsibilties:

• Perform analytical tasks that include data gathering, analysis, visualization, and data-driven storytelling as a basis of project justification and innovation.
• Perform statistical/machine learning projects as necessary for given business needs. These projects may consist of – large scale/rapid hypothesis testing, classification, prediction, and recommender systems.
• Develop dynamic, productionized, and scalable customer-level models that generate ROI for both DG and their customers. These models may include predictive propensity models and customer segmentation
• Serve as a leader on the team providing direction, sharing knowledge, offering analytical expertise, and mentoring junior team members as appropriate

Qualifications

Knowledge, Skills and Abilities: 

• 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
• Experience with customer-level modeling preferred
• Knowledge of statistical techniques and concepts (properties of distributions, statistical tests and proper usage, etc.)

Work Experience &/or Education: 

• MS in Data Science, Statistics, Economics, Computer Science, Mathematics, or related applied quantitative field preferred.
• 8+ year’s 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
• 3-4 years of leading /developing statistical models