Data Scientist (ML/NLP Developer)

Key Responsibilities:

• Develop NLP Solutions: Build and deploy advanced NLP models for tasks such as text categorization, named entity recognition, sentiment analysis, and document classification.
• Enhance Credit Scoring Models: Contribute to the development and optimization of ML models for credit risk assessment and scorecard development.
• Data Exploration & Preprocessing: Process and analyze unstructured text data using NLP techniques, including tokenization, lemmatization, word embeddings, and vectorization.
• Model Research & Development: Collaborate with data scientists and researchers to develop, evaluate, and refine deep learning and transformer-based models (e.g., BERT, GPT) to solve complex business challenges.
• Product Integration & Optimization: Work closely with engineering and product teams to integrate ML/NLP models into production, ensuring scalability and efficiency.
• Research & Innovation: Stay up to date with advancements in NLP, ML, and AI research, translating cutting-edge techniques into practical, real-world applications.

Required Skills and Experience:

• NLP & Deep Learning Expertise: Min 2 years of experience in NLP and ML, particularly in text classification, entity recognition, and semantic analysis.
• ML Frameworks Proficiency: Strong experience with frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and Keras.
• Python Programming: Proficiency in Python and familiarity with libraries such as NLTK, spaCy, scikit-learn, NumPy, pandas, and OpenAI’s API.
• Database & API Integration: Experience with SQL and NoSQL databases, as well as developing and integrating RESTful APIs.
• Problem-Solving Mindset: Strong analytical skills with the ability to independently solve complex NLP challenges.
• Hands-on expertise with Regular Expressions.

Preferred Qualifications:

• Qualification: BE, BTech, MTech, ME, MCA (minimum 4 years of degree)
• Experience: 1-2 Years
• Continuous Learning: A strong desire to stay updated with the latest research and advancements in AI, ML, and computer vision.