This key role within Decision Science & Analytics will lead the ongoing development and execution of the Company’s customer and marketing analytics program. This role will work collaboratively with key third parties, internal IT resources and the broader Decision Science organization to analyze transactional data, develop predictive and deterministic models to deliver insights.
Duties & Responsibilities:
- Perform analytical tasks that include data gathering, analysis, visualization, and data-driven story-telling 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 propensity models that generate ROI for both DG and their customers
Knowledge, Skills and Abilities (KSAs):
- Strong problem-solving skills utilizing expertise, business judgment and robust quantitative analyses.
- Must have proficiency with common analytical platforms (e.g. Hadoop, Spark, H20.ai, Snowflake, etc.) used to solve clustering, classification, regression, anomaly detection, simulation and optimization problems on large scale data sets.
- Experience with regularized regression, random forest, boosting methods and other statistical/machine learning methods
- Demonstrated ability to translate complicated topics into easily communicable concepts.
- Practical experience ingesting and manipulating large volumes of data (both tall and wide).
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 (via R, H2O, Python, Spark, etc.)