Vacancy expired!
We are looking for a Sr. Data Scientist to be located in our office at Eden Prairie, MN. As a Data Scientist for Marketing, you will be at the forefront of delivering advanced analytics solutions and machine learning models across the Marketing organization. You will focus on utilizing data from multiple sources to discover new customer insights, drive marketing communications, tailor the customer experience, and ultimately drive revenue. You will be an expert for the Marketing organization on predictive modeling, customer analytics, statistical methodologies, and testing and measurement. Our ideal candidate should be a strong programmer, be familiar with modern modeling techniques, have a deep understanding of and appreciation for data, possess excellent communication skills, and be passionate about solving business problems with data.
DUTIES AND RESPONSIBILITIES:Develop and/or implement machine learning models for predictive purposes.Validate models for roll-out and analyze the performance of existing models.Assess the usefulness of new data sources, transformations, and modeling techniques.Perform high quality, data driven statistical inference.Mine large datasets in multiple databases to draw patterns and inferences to build models and arm leaders with information that will help make business decisions.Support end-users to design strategies based on newly developed machine learning models.Find new areas of opportunity for Marketing to utilize predictive models or other analytic solutions.QUALIFICATIONS:Minimum 5 years experience in data science or 7 years in data analyticsStrong skills in SQL or SASExperience with Python or RA passion for evaluating business problems and generating data-driven solutionsA deep, intuitive understanding of data and its quirks.Strong problem solving, project management, and communication skillsHands on experience with information retrieval from high dimensional data, dimensionality reduction and feature extraction.Basic knowledge of supervised and/or unsupervised model building techniques is a plus, especially one of these machine learning methodologies, such as: neural networks, extreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosted models (GBM), and random forest.Able to work alone and on a team. Should feel comfortable tackling ambiguous problems with limited direction.Vacancy expired!