Design and build new NLP pipelines for modeling, data mining, and production of complex answers.
Design, develop, and deploy algorithms based on ML and NLP best practices in order to tackle hard problems where structured and unstructured data is involved.
Perform and interpret ML and NLP studies and product experiments concerning new data sources or new uses for existing data sources.
Develop prototypes, proof of concepts, algorithms, predictive models, and custom analyses.
Search for patterns in structured and unstructured data that can provide solutions to business problems or create new business opportunities.
Use advanced computational techniques to analyze vast pools of data under the limited guidance of the lead/principal data scientist.
Communicate clearly and effectively with both clients and multi-disciplinary teams.
Strengthening the artificial intelligence team’s capabilities through machine learning mastery.
Masters degree (MSc) in Computational Linguistics, Cognitive Science, Computer Science, or related field.
Experience using Python (pandas, scikit-learn, mlpy, NLTK, gensim, spaCy, TextBlob, Orange, etc.), or other scripting languages (such as Java: Mallet, Apache OpenNLP, Stanford Topic Modeling Toolbox, etc.) for data preparation, analysis, and machine learning (classification, regression, clustering, dimensionality reduction).
Experience working on one or more projects comprising one or more components such as big data, text mining, and statistical machine learning.
Experience with text classification, representation learning, and language modeling techniques, such as the Word2Vec, GloVe, and FastText algorithms.
Knowledge of algorithms and techniques of a computational domain with emphasis on text processing, performance, and scalability.
Working knowledge of database design and interaction (such as SQL/relational databases).
Ability to cope under high demand, handle multiple priorities, projects and tasks, and meet tight deadlines.
Strong oral and written communication skills. Must be able to interact cross-functionally and drive both business and technical discussions.
Preferred Qualifications :
Doctoral degree (PhD) in Computer Science, Mathematics, Engineering, or related field.
Knowledge and experience of deep learning techniques in artificial intelligence, with libraries such as TensorFlow, PyTorch, Caffe2, Keras, or Theano.
Experience with cloud computing infrastructure (AWS, MS Azure, etc.)