Lead Assistant Manager

Role : Data Scientist -NLP Basic Qualifications 2+ years of experience in data science with a focus on Natural Language Processing (NLP) and text data analysis Proficiency in NLP techniques such as tokenization, lemmatization, part-of-speech tagging, named entity recognition (NER), sentiment analysis, and language modeling Strong expertise in machine learning algorithms including supervised and unsupervised learning (e.g., classification, clustering, dimensionality reduction) Solid experience in programming languages like Python , R , or Java , with deep knowledge of NLP libraries (e.g., spaCy , NLTK , Transformers , Gensim , Hugging Face ) Experience working with deep learning frameworks (e.g., TensorFlow , PyTorch ) for NLP model development, training, and fine-tuning Familiarity with cloud computing environments, particularly Google Cloud Platform (GCP) or AWS , for scaling machine learning models Strong understanding of data preprocessing , text vectorization (e.g., TF-IDF , Word2Vec , BERT embeddings ), and feature engineering for NLP tasks Expertise in SQL and experience working with structured and unstructured data from diverse sources Strong problem-solving skills, with the ability to work independently and innovate based on business needs and data insights Proven ability to collaborate with cross-functional teams, including engineering and business teams, to translate business objectives into data-driven solutions Preferred Qualifications Degree in Computer Science , Data Science , Statistics , Mathematics , or a related field, or equivalent work experience Hands-on experience with transformer-based models (e.g., BERT , GPT , T5 , RoBERTa ) for NLP tasks like text classification, summarization, translation, and generation Expertise in text mining and experience with large-scale text data processing and analysis Experience with advanced data visualization techniques and tools such as Tableau , Matplotlib , Seaborn , or Plotly for presenting NLP model insights Knowledge of NLP deployment strategies and frameworks (e.g., FastAPI , Flask , ONNX ) for serving machine learning models in production environments Familiarity with knowledge graphs , semantic search , and information retrieval techniques for enhancing NLP applications Experience in optimizing and fine-tuning NLP models for performance, scalability, and efficiency in real-world applications Understanding of data privacy , security , and ethical implications of using NLP in production systems A passion for staying up-to-date with the latest research and advancements in NLP and AI Strong communication skills , with the ability to effectively present technical findings and insights to both technical and non-technical stakeholders Ability to thrive in an Agile environment , collaborating across teams, iterating quickly, and adapting to new challenges