Data Scientist

GDIA Mission and Scope-

The Global Data Insights and Analytics (GDI&A) department at Ford Motor Company is looking for qualified people who can develop scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization. The goal of GDI&A is to drive evidence-based decision making by providing insights from data. Applications for GDI&A include, but are not limited to, Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Warranty Analytics.

Potential candidates should have hands-on experience in applying first principles methods, machine learning, data mining, and text mining techniques to build analytics prototypes that work on massive datasets. Candidates should have experience in manipulating both structured and unstructured data in various formats, sizes, and storage-mechanisms. Candidates should have excellent problem-solving skills with an inquisitive mind to challenge existing practices. Candidates should have exposure to multiple programming languages and analytical tools and be flexible to using the requisite tools/languages for the problem at-hand.

Qualifications:

• BE, B.Tech, M.S. or Ph.D. in Engineering, Computer Science, Operations research, Statistics, Applied mathematics, or in a related field
• 3+ years of experience in at least one of the following languages: Python, R, MATLAB, SAS
• 3+ years of hands-on experience in using machine learning/text mining tools and techniques such as Clustering/classification/decision trees, Random forests, Support vector machines, Deep Learning, Neural networks, Reinforcement learning, and other numerical algorithms
• Experience with GoogleCloud Platform (GCP) including VertexAI, BigQuery, DBT, NoSQL database
• Excellent problem solving, communication, and data presentation skills

Key Roles and Responsibilities of Position:

• Build data-driven models to understand the characteristics of engineering systems
• Apply machine learning, data mining and text mining techniques to create scalable solutions for business problems
• Train, tune, validate, and monitor predictive models
• Analyze and extract relevant information from large amounts of historical business data especially related to quality, product development, and connected vehicles, both in structured and unstructured formats
• Establish scalable, efficient, automated processes for large scale data analyses
• Package and present the findings and communicate with large cross-functional teams