[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL] | GitHub: [Your GitHub URL]

PROFESSIONAL SUMMARY
NLP Data Scientist with [X] years building language models and text analytics solutions. Expert in transformers, BERT, and GPT with proven ability to improve accuracy by [Y%]. Strong track record deploying NLP systems processing [X]M documents daily.

TECHNICAL SKILLS
NLP Techniques: Text Classification, Named Entity Recognition, Sentiment Analysis, Question Answering, Machine Translation, Text Summarization
Models & Architectures: BERT, GPT, T5, RoBERTa, DistilBERT, LSTM, Transformers
Frameworks: Hugging Face Transformers, spaCy, NLTK, Gensim, AllenNLP
Languages: Python, Java, R
ML/DL: PyTorch, TensorFlow, Scikit-learn
Tools: Elasticsearch, AWS Comprehend, Google Cloud NLP

PROFESSIONAL EXPERIENCE

NLP Data Scientist / NLP Engineer
[Company Name], [City, State]
[Start Date] – Present
• Developed chatbot handling [X]K conversations daily with [Y%] intent recognition accuracy
• Built named entity recognition system extracting [X] entity types with [Y%] F1 score
• Implemented sentiment analysis model processing [X]M reviews with [Y%] accuracy
• Fine-tuned BERT model for domain-specific task improving baseline by [Z%]
• Created text classification pipeline supporting [X] categories with [Y] ms latency
• Deployed models serving [X]M predictions daily through REST API

NLP Data Scientist
[Previous Company Name], [City, State]
[Start Date] – [End Date]
• Built document classification system organizing [X]M documents with [Y%] accuracy
• Developed entity extraction model identifying [number] key information types
• Implemented machine translation system for [languages] with [metric score]
• Created topic modeling solution discovering [X] themes from [Y]K documents
• Reduced model size by [Z%] while maintaining [accuracy level] through distillation

TECHNICAL PROJECTS

Conversational AI System
• Built end-to-end chatbot using [architecture] handling [X] intents
• Integrated with [platforms] serving [Y]K users
• Achieved [metric] satisfaction score and [Y%] containment rate

Text Summarization Pipeline
• Implemented abstractive summarization using [T5, BART, etc.]
• Processed [X]K documents reducing reading time by [Y%]
• Deployed with [Z] ms latency per document

Question Answering System
• Developed extractive QA using [BERT-based model]
• Achieved [X] F1 score on [benchmark or custom dataset]
• Integrated with [search/knowledge base system]

EDUCATION
[Degree Name], [Field]
[University Name], [City, State]
Graduation: [Year]

PUBLICATIONS
• [Paper on NLP topic], [Venue], [Year]

CERTIFICATIONS
• [NLP Specialization], [Platform], [Year]
• [Deep Learning Certificate], [Year]

TECHNICAL EXPERTISE
• Text Preprocessing: Tokenization, lemmatization, stop word removal, text normalization
• Feature Engineering: TF-IDF, Word2Vec, GloVe, FastText embeddings
• Model Development: Transfer learning, fine-tuning, prompt engineering
• Evaluation: BLEU, ROUGE, perplexity, F1 score, accuracy metrics