Data Science Internship
Mercedes-Benz Financial Services USA LLCFarmington hillsUpdate time: December 13,2019
Job Description

About Us:
Mercedes-Benz Financial Services is a leading captive automotive financial services provider within Daimler Financial Services, which operates in 40 countries as the global financial services arm of Daimler AG.  In the U.S., the corporate headquarters for the Americas Region, we operate under the brands of Mercedes-Benz Financial Services and Daimler Truck Financial. In the Americas Region we also have business operations in Canada, Mexico, Argentina and Brazil.


To achieve our goal of being the first-choice provider of vehicle financial products and services that support our brands, dealers and customers, we are looking for talented, diverse and inspired people with the skills, experience and innovative spirit to provide a first-class experience at every turn. We are committed to providing a great workplace environment that fosters a culture of openness, support and camaraderie. Our employee programs and benefits are reflective of our values and are designed to promote growth, balance and wellness in many aspects of your career and life. To see other programs and benefits we offer, visit our website at www.mbfs.com.


Primary Purpose: 
Main responsibility will be to help current data scientist in data preparation, model development, creating machine learning algorithms using R and/or Python and other tools.
  
Major Projects: 
Student will be involved in data preparation, model development and creating machine learning algorithms using R or Python and other tools.
 
List of Activities:
Student will be involved in team meeting and shadowing data scientists and data visualization team members.

Please note this is a seasonal internship that will work up to 90 calendar days in our Farmington HIlls, Ml office.  Candidate must be currently enrolled or have graduated within a year of starting internship.


Internship will run from May to August 2020. Start date and end date are flexible based on candidates availability.


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