Ford Motor Company is looking to leverage data science, particularly LLMs and advanced analytics, to solve critical manufacturing challenges, optimize processes, and improve decision-making through better utilization of manufacturing and OT data.
Requirements
- Expert proficiency in Python (Numpy, Pandas, Scikit-learn, TensorFlow/PyTorch) for data manipulation, analysis, and model development.
- Deep knowledge of LLM architectures and practical application frameworks (e.g., Hugging Face Transformers, LangChain, LlamaIndex).
- Strong SQL skills for complex data extraction and manipulation.
- Expertise in various machine learning and deep learning techniques, especially those applicable to time-series data, anomaly detection, and natural language processing.
- Familiarity with MLOps principles, CI/CD for ML pipelines, and model monitoring in production.
- Understanding of cloud platforms (GCP) and their relevant data/AI services, particularly for hybrid cloud/edge deployments.
- Proven hands-on experience with Large Language Models (LLMs), including prompt engineering, fine-tuning, and practical application in real-world scenarios.
Responsibilities
- Define the strategic roadmap for applying data science, particularly LLMs and advanced analytics, to critical manufacturing challenges.
- Oversee the end-to-end lifecycle of data science projects, from problem definition and data acquisition to model development, deployment, and continuous monitoring.
- Translate complex manufacturing challenges (e.g., predictive maintenance, quality defect prediction, process optimization, root cause analysis, production scheduling) into actionable data science initiatives.
- Apply a wide range of data science techniques, including advanced statistical modeling, machine learning, and deep learning, to deliver robust and scalable solutions.
- Drive the exploration and implementation of Large Language Models (LLMs) to unlock insights from unstructured manufacturing data (e.g., maintenance logs, quality reports, operator notes, safety incident reports, technical documentation).
- Lead initiatives in prompt engineering, fine-tuning LLMs for manufacturing-specific tasks, and developing Retrieval Augmented Generation (RAG) systems to enhance knowledge retrieval and decision support.
- Influence and guide the strategy for collecting, structuring, and accessing high-quality, real-time data from OT systems to ensure it meets the demands of advanced analytics and AI models.
Other
- 8+ years of progressive experience in Data Science, with a significant portion in a leadership or lead contributor role.
- 5+ years of direct experience applying data science within a manufacturing or industrial environment, ideally automotive.
- Strong strategic thinking and problem-solving abilities, capable of navigating ambiguity and driving results in a complex environment.
- Excellent verbal and written communication, presentation, and interpersonal skills, with the ability to influence cross-functional teams and senior leadership.
- A proactive, curious, and results-oriented mindset.
