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 Statistics, Econometrics, Machine Learning, Big Data, 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.
Roles & Responsibilities
• Develop statistical methodologies and deploy analytical tools to support different business initiatives
• Continual enhancement of statistical techniques and their applications in solving business objectives
• Compile and analyze the results from modeling output and translate into actionable insights
• Acquire and share deep knowledge of data utilized by the team and its business partners
• Participate in global conference calls and meetings as needed and manage multiple customer interfaces
• Execute analytics special studies and ad hoc analyses
• Evaluate new tools and technologies to improve analytical processes
• Set own priorities and timelines to accomplish projects (accountability for project deliverables)
Efforts will focus on the following key areas:
• Various classical Statistical techniques such as Regression, Multivariate Analysis etc.
• Data Mining & Text Mining
• Time Series based forecasting modeling
• Customer Segmentation – Cluster & Factor Analysis
• Customer Relationship Management Analytics
• Demand Modeling
• Statistical Scoring models to be used in Risk/Marketing domains
• Market Mix Modeling
• Structural equation modeling for identifying accelerators of customer satisfaction and loyalty
Key behaviors:
• Enthusiastic and self-motivated, with the ability to lead projects proactively
• Meticulous attention to detail, with an overall passion for continuous improvement
• Innovative and creative, with a logical and methodical approach to problem-solving
• Credible and articulate, with excellent communication, presentation, and interpersonal skills
• Ability to multi-task and manage competing priorities
Educational Qualification:
Advanced degree (Masters or PhD) in Statistics / Econometrics/ Operation Research / Finance / Actuarial Science or any other quantitative discipline or an Engineering degree with MBA from a premier institute and quantitative emphasis
- Should have experience in CAE related Deep learning processing
- Should be comfortable with CNN’s and related architectures
- Comfortable with data augmentation and pre-processing related to CV specific problems
- Programming background with C, Python
- Ability to debug Tensorflow error codes