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Data Scientists Modeling Sep 26, 2018
Dearborn, MI 48124  
  Requirements
Position Description:
The Global Data Insights and Analytics (GDI&A) Product Analytics team supports Ford’s Product Development organization with analytical solutions. We are looking for a data scientist with an expertise in Machine and Statistical Learning. As a member of this dynamic team, you will have the opportunity to work with some of the brightest global subject matter experts in product development and vehicle connectivity that are transforming the automobile industry. We are seeking a Machine Learning scientist to assist in all phases of project work, including problem formulation, model development, and deployment for use in support of Ford Design, Product Development and Connectivity initiatives. The job candidate should have great autonomy, exceptional collaboration skills, and self-discipline to conduct original research and choose appropriate methodologies to solve related problems. Responsibilities: • Analyze source data and data flows, working with structured and unstructured data • Manipulate high-volume, high-dimensionality data from varying sources to highlight patterns, anomalies, relationships and trends • Collaborate with Product Development experts to understand their requirements and overall business needs • Apply Machine Learning technology to solve real-world automotive related problems • Communicate and present models to business customers and executives • Collaborate internally and externally to identify new and novel data sources and explore their potential use in developing actionable business insights • Explore emerging technologies and analytic solutions for use in quantitative model development
Skills Required:
2+ years of experience in at least one of the following languages: R, Python, MATLAB, Java, C/C++/C#, SAS
Skills Preferred:
• 2+ years of experience with SQL, Spark, OR Hadoop • 1+ year of post-graduate work experience (in a business or post-doc setting) involving complex quantitative modeling and analysis in any of the areas mentioned under Basic Qualifications • Demonstrated ability in the application of Machine Learning in real-world industrial settings with large scale data • Experience with Deep Learning frameworks such as TensorFlow, Caffe, Caffe2, PyTorch, or MxNet • Strong oral, written and interpersonal communication skills and an ability to work in a team environment • Comfortable working in a fast paced and innovative environment where problems are not always well-defined
Experience Required:
2+ years of experience with Machine Learning models and tools to include one or more of the following: neural networks, deep learning, regression, classification, or clustering
Experience Preferred:
0
Education Required:
Master’s degree in Computer Science, Computer Engineering, Statistics, Economics, Mathematics or Physics or related field
Education Preferred:
PhD in a quantitative field such as Computer Science, Computer Engineering, Statistics, Economics, Mathematics, Physics
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December 19, 2018