Data Scientist (Mid to Senior Level)

Requirements

• Design and implement NLP and LLM-based solutions, including chatbots, sentiment analysis, information extraction, and automated summarization.
  
• Develop and optimize machine learning models, including deep learning architectures (CNNs, RNNs, LSTMs, Transformers) and multimodal systems (e.g., CLIP).
  
• Perform text preprocessing tasks such as tokenization, embeddings, stemming, and lemmatization.
  
• Conduct fine-tuning and prompt engineering for LLMs to enhance performance for specific use cases.
  
• Build and deploy recommender systems and time series forecasting models to drive business value.
  
• Collaborate with cross-functional teams to integrate data science solutions into production environments using MLOps practices.
  
• Leverage cloud platforms and ML services to scale and operationalize data science workflows.
  
• Analyze large datasets to extract actionable insights and communicate findings to stakeholders.
  

• Experience Level: Mid to Senior (4+ years of relevant experience in data science, NLP, or machine learning).
  
• NLP & LLMs Expertise:
  
• Proficient in text preprocessing techniques (tokenization, embeddings, stemming/lemmatization).
  
• Experience with classical NLP models (TF-IDF, Word2Vec, GloVe) and transformer-based models (BERT, GPT, RoBERTa).
  
• Hands-on experience in fine-tuning LLMs and prompt engineering.
  
• Practical application development in areas such as chatbots, sentiment analysis, information extraction, and summarization.
  
• Advanced AI Knowledge:
  
• Strong understanding of deep learning frameworks (CNNs, RNNs, LSTMs, Transformers).
  
• Familiarity with multimodal Vision + NLP models (e.g., CLIP).
  
• Experience with recommender systems and time series forecasting.
  
• Knowledge of reinforcement learning is a plus.