Ford Data Scientist - NLP Modeling 

Mexico, State of Mexico, Nezahualcóyotl 

758039677

Responsibilities:

• Develop and deploy NLP models to extract insights from unstructured textual data.
• Collaborate with cross-functional teams to identify opportunities and develop strategies for applying NLP techniques to enhance quality analytics.
• Design and implement data preprocessing and feature engineering techniques for NLP tasks.
• Utilize supervised and unsupervised machine learning techniques to solve complex NLP problems.
• Evaluate and fine-tune models for performance optimization, accuracy, and efficiency.
• Contribute maintainable code to existing and new pipelines.
• Stay up to date with the latest advancements in NLP and contribute to the continuous improvement of our methodologies and algorithms.
• Communicate findings, insights, and recommendations to stakeholders in a clear and concise manner.

Minimum Requirements:

• Master's degree in computer science, Engineering, Data Science, Statistics, Applied Mathematics, or a related data field.
• 2+ years of hands-on experience in Python programming for data analysis and machine learning.
• 1+ years’ experience in SQL programming language and relational databases.
• 2+ years of experience with NLP libraries and frameworks such as NLTK, spaCy, and Gensim.
• 2+ years of experience with both supervised and unsupervised machine learning techniques.

Preferred Requirements:

• PhD in Computer Science, Engineering, Data Science, Statistics, Applied Mathematics, or a related data field.
• Experience working with large language models such GPT-4, Palm, Llama-2, etc.
• Knowledge of deep learning frameworks such as TensorFlow or PyTorch.
• Industry experience in NLP modeling, preferably within the automotive or related domains.
• Experience with Git and GitHub for version control and collaboration.
• Experience with Google Cloud Platform for data processing and machine learning tasks.
• Strong communication skills, with the ability to effectively collaborate with cross-functional teams and communicate complex technical concepts to non-technical stakeholders.