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Lead, Data Science - General Motors

General Motors


Location:
Detroit, Michigan
Date:
04/13/2017
Categories:
  • Data Analytics
  • Data Scientist
General Motors
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Job Details

he Data Science and Analytics Department in The Global Connected Customer Experience (GCCX) division of General Motors is responsible for all Analytical Data Sets, Analytics, and Data Science required to provide customers with the best overall service and experience in the industry, delivering increased customer satisfaction, loyalty, and optimization of spending. This includes GM's market-leading connectivity and infotainment products and services such as OnStar safety, security and services brand, which recently passed 1.5 billion customer interactions, as well as GM’s Maven initiative, which is developing solutions to capitalize on the future of personal mobility, including car sharing. The solutions developed by the Data Science and Analytics team are implemented across a number of touch points which may include web-sites, dealerships, contact centers and in-vehicle applications. This is a unique opportunity to be a part of new team that will develop technical strategies and drive value from a big data approach to advanced analytics, machine learning and/or cognitive computing.

 

Responsibilities Will Include Some Or All Of The Following

  • Supporting diverse technical teams to ensure project deliverables fulfill business needs are on-time, effectives and meet business requirements - Contributing to the definition of an overarching big-data-driven approach to advanced analytics strategy and architecture - Working with cross-functional teams to discover and develop actionable, high-impact data analytics need and data opportunity statements in a variety of core business areas - Managing the development of industry leading solutions including: 1. Creating breakthrough solutions, performing exploratory and targeted data analyses to drive iterative learnings 2. Working with business domain, IT and data experts to identify detailed data needs, sources, and structure to support solution development - Fulfilling technical requirements and data analytic activities - Synthesizing large scale data sets (100’s of terabytes) from multiple structured and unstructured data sources.

     

The policy of General Motors is to extend opportunities to qualified applicants and employees on an equal basis regardless of an individual's age, race, color, sex, religion, national origin, disability, sexual orientation, gender identity/expression or veteran status. Additionally, General Motors is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us at Careers.Accommodations@GM.com. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

Requirements

Requirements

  • Experience with SAS, SQL, Python, Alteryx, Hive/Hadoop and/or other data tools
  • Academic experience in one or more of the following fields: physics, engineering, bio-statistics, astrophysics, computational chemistry, cognitive or neurosciences, behavioral economics, econometrics, finance, mathematics, computer science.
  • Bachelor’s degree (Master’s degree preferred)
  • Experience supporting diverse and creative technical teams in a fast paced environment

     

Business And Problem Solving Skills Of Interest

  • Ability to prioritize and manage multiple tasks and projects at once without sacrificing quality
  • Strong listening and communications skills
  • Highly collaborative work style
  • Ability to evaluate the big picture and solve business problems rather than focusing only on metrics

     

Technical Experience Of Interest

  • Familiar with data aggregation and manipulation using large data formats, e.g., SQL on Hadoop
  • Knowledge of data quality, data cleansing, data wrangling, and data standards.
  • Ability to troubleshoot data quality and data integrity issues
  • Work experience in a data-intensive industry such as biotechnology, engineering, astrophysics or particle physics experiments, quantitative finance or high frequency trading, intelligence analytics