Senior Data Scientist

Experience :

Location :

Designation :

Role Overview:

Key Responsibilities:

• NLP & LLM Model Development: Build, fine-tune, and deploy advanced NLP modelsincluding text classification, document analysis, entity recognition, and sentiment analysisleveraging state-of-the-art architectures (BERT, GPT, Llama, etc.).
• LLM Research & Customization: Explore, adapt, and optimize pre-trained LLMs (e.g., OpenAI, Hugging Face, Llama).
• Credit & Risk Analytics: Develop, refine, and evaluate ML models for credit scoring, scorecard generation, and risk analytics.
• Data Exploration & Processing: Analyze and preprocess large amounts of unstructured text using tokenization, language normalization, embeddings, and vectorization.
• Deployment & Integration: Collaborate with engineering/product teams to integrate NLP/ML/LLM solutions into real-world products, ensuring scalability and reliability.
• Continuous Innovation: Stay current on advances in NLP, ML, and generative AI, proactively translating research breakthroughs into practical applications.
• Mentorship & Collaboration: Guide junior team members and collaborate across data, product, and engineering groups.

Required Skills & Experience:

• Work Experience: 34 years as a data scientist, NLP engineer, or ML specialist with hands-on experience in NLP and LLMs.
• NLP & Deep Learning: Proven expertise in NLP tasks (text classification, entity recognition, semantic analysis) and deep learning, especially with transformer architectures.
• Large Language Models: Experience working with, training, and deploying LLMs (GPT, Llama, BERT, etc.).
• ML Frameworks: Advanced skills in Python with frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, and Keras.
• Tooling: Experienced with libraries (NLTK, spaCy, scikit-learn, NumPy, pandas, OpenAI API).
• Database & API Integration: Proficient with SQL/NoSQL databases and RESTful API development/integration.
• Problem Solver: Strong analytical, debugging, and creative solution skills for real-world ML/NLP challenges.
• Regular Expressions: Hands-on expertise with text processing using regex.

Preferred Qualifications:

• Education: BE/BTech/ME/MTech/MCA in Computer Science or related field (minimum 4-year degree).
• Continuous Learner: Genuine interest in ongoing research and upskilling in AI, ML, GenAI, and computer vision.