Data Analyst

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. Candidate should have proficiency in Python, expertise in Entity extraction, document classification, Natural Language Processing (NLP) & Natural Language Generation (NLG). Experience of developing commonly used predictive models like - Linear Regression, Decision Trees, SVM. KNN, Gradient Boost, Random Forest etc.

Proficiency in cloud platforms like GCP, including experience with deploying AI applications.

Solid programming skills in Python and experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

Proven track record of delivering successful Gen-AI projects and driving business impact.

Strong understanding of text pre-processing & normalization techniques such as tokenization

Excellent communication, presentation, and documentation skills.

Strong problem-solving abilities and a proactive attitude towards learning and adopting new technologies.

Excellent problem solving, communication, and data presentation skills

• 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