Analytical Modeler

6 days ago

Chennai Tamil Nadu, India Ford Motor Company Full time

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.

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.
- 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
- 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
- 1+ years of experience in developing LLM solutions for Enterprise
- Experience with GoogleCloud Platform (GCP) including VertexAI, BigQuery, DBT, NoSQL database and Hadoop Ecosystem
- Excellent problem solving, communication, and data presentation skills
- Build data-driven models to understand the characteristics of engineering systems
- 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

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