What you'll do:

• Must have 5-8 years of relevant experience as Data Scientist.
• Experience in extracting data / information, through complex semi-structured and unstructured documents using NLP & Parsing
• Analyzing business problem and cut through the data challenges
• Ability to churn the raw corpus and develop a data/ML model to provide business analytics (not just EDA), machine learning based document processing and information retrieval.
• Quick to develop the POCs and transform it to high scale production ready code.

What makes you a great fit:

• NLP, Scraping, Parsing including libraries such as NLTK, Gensim, Spacy, Scrapy, beautifulsoup, regex etc.
• Deep Learning including Keras, TensorFlow / PyTorch, Neural Networks, such as CNN, LSTM/GRU/RNN/CNN/GAN/Residual Networks etc.
• Generative AI, Transfer Learning, Transformers, Embeddings, LLMs, Prompt Engineering, Encoders, Decoders etc.
• Supervised, unsupervised, semi-supervised, few shot / zero shot learning including EDA, training, modelling, hyper-parameter tuning, API creation etc. in Regression & Binary/Multiclass classifications in algorithms such as Decision Trees, SVM, XGBoost etc.
• Python data structures using List, tuple, dictionary, collections, iterators, Pandas, NumPy etc. including libraries such as Scikit-learn, imblearn, SciPy etc.
• Database & SQL knowledge (like Postgres, SQL Server, MySQL etc)
• AWS services like EC2, Beanstalk, Lambda including Containerization, Docker images etc.
• Generative AI, Transfer Learning, Transformers, Embeddings, LLMs, Prompt Engineering, Encoders, Decoders etc.
• Object oriented programing(OOP) & Rest API), CI/CD/CT, MLOps