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Data Scientist - eCommerce Analytics

Posted

DICK'S Sporting Goods
Headquarters: Coraopolis, PA
https://www.dickssportinggoods.com/
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The data scientist in the eCommerce department performs detailed analyses on our online customer in order to make recommendations on how to increase business growth. The data scientist will focus on blending various data sources and applying machine learning models and data mining techniques to create these insights and new processes.
Qualifications
Machine Learning Applications & Analyses
  • Apply machine learning and data mining techniques to extract actionable insights from large-scale, high-dimensional data.
  • Build and put into deployment algorithmic solutions that focus on improving the customer’s experience both in-store and on our web properties.
  • Compile data from desperate data sources leveraging both qualitative and quantitative data to build holistic views of customer’s experience.
  • Distill these complex analyses into presentations and recommendations for non-technical business units.
Data and Reporting
  • Support eCommerce leadership and business units with critical data for ad-hoc requests to inform business decisions.
  • Develop and support reporting needs through construction of automated dashboards in conjunction with the data and reporting team
Client Liaison
  • Work with a variety of business units within the eCommerce department to help translate their requirements into specific analytical deliverables.
  • Continue to improve and advance communications and collaborations amongst the various analytics teams and eCommerce business units.
Training
  • Lead or support formal and/or informal training for team members on the various tools used for team members within the analytics team or client teams.
Master's Degree
Statistics, Computer Science, Engineering, Mathematics, Economics, or other quantitative field
  • Additional experience will be considered in lieu of an advanced degree.
1-3 years of experience
Python or R experience
  • Statistics/Machine Learning with applications in R or Python.
  • Creating visualizations and presentations for non-technical users.