Senior Data Scientist
1 day ago
As a Senior Data Scientist - NLP Expert at MICHELIN, you will be responsible for developing and implementing NLP solutions that drive business value. You will work closely with cross-functional teams to understand business needs and develop innovative solutions using NLP methodologies such as document embedding, text mining, topic classification, sentiment analysis, information extraction, conversational AI, and generative AI.
Key Responsibilities:
- Develop and implement NLP solutions using Python and deep learning models for sequence-to-sequence problems like LSTMs, GRUs, RNNs.
- Work with NLTK, Spacy, Gensim, HuggingFace Transformers to build and deploy NLP models.
- Collaborate with data engineers to integrate NLP models with data pipelines and deploy them in a cloud-based environment.
- Develop and maintain high-quality code, ensuring reusability and maintainability.
- Stay up-to-date with the latest NLP advancements and apply them to real-world problems.
Requirements:
- 9+ years of overall machine learning/advanced analytics experience with considerable time spent in building enterprise-grade NLP solutions.
- 6+ years of NLP solution building and applied research experience including 4+ years in Python.
- Strong experience developing and implementing NLP applications in a commercial setting.
- Hands-on experience in building POCs and solutions using Generative AI and LLMs.
- Expert in NLP methodologies: document embedding, text mining, topic classification, sentiment analysis, information extraction, conversational AI, generative AI, etc.
- Self-starter who can define precise problems and implement optimal solutions.
- Expert in deep learning models for sequence-to-sequence problems like LSTMs, GRUs, RNNs.
- Experience with NLTK, Spacy, Gensim, HuggingFace Transformers.
- MTech/BTech/B.E. degree in Computer Science or related IT/technology field or equivalent experience.
- Excellent English communication skills, both oral and written.
- Team player who can work well in a highly collaborative, multi-cultural, and matrixed organization.
Preferred:
- Azure knowledge and conceptual familiarity with ML Engineering and ML Ops.
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