As a Lead Data Scientist: NLP you will lead both research and development functions of the team. You will focus on designing, implementing and maintaining various Deep Learning and Reinforcement Learning code, in addition to the development of common NLP technologies such as Text Classification, Tokenization, Part of Speech tagging, Text Mining, Named Entity Recognition. You will be responsible for leading end-to-end solution development, including machine learning research, and software design (architecture) and development.
Who you are
You have an end-to-end hands-on ownership of ML features and various projects.
You create POCs focused on Deep Learning as well as other traditional approaches within Data Science.
You develop algorithms in key areas in Machine Learning and Deep Learning.
You are a Team-Player working closely with other colleagues while maintaining a clear vision of the value of our diverse offerings.
You lead the path of value-driven AI features that are innovative and deployable to real customers.
You embrace the latest cutting-edge AI technologies.
You transform ambiguity into clarity.
You enjoy collaborating in a multicultural and diverse environment that expands to include various geographic locations and spans numerous cultures.
You have stellar communication skills, effectively expressing yourself. You convey and receive information in a clear, credible and consistent manner.
Desired Qualifications & Expereince
PhD computer science, or a related field with 8 years experience in the areas of Deep Learning, Machine Learning and NLP
3 years experience of software development.
2 years experience in leadership position.
Research publications in top CV/ML conferences or journals
Strong understanding of Deep Learning methods and practices (architectures, activation functions, normalization techniques, attention mechanisms, etc.).
Strong understanding of statistical learning techniques (Classification, Regression, Clustering, metrics, structured learning, probabilistic graphical models).
Strong understanding of model architectures of Deep Neural Networks (Gradients, Activation Paths, Activation Functions, etc.).
Track record of solving industry problems through novel algorithms and innovation.
Fast prototyping skills, including comprehensive feature integration during all cycles of development.
Strong understanding with graph databases such as Neo4J.
Excellent skills in software development in Python, Java, C/C for prototyping and integration
Hands-on experience with AI frameworks (Scikit-learn, PyTorch, TensorFlow, Caffe, Theano, Torch, R, TensorFLow, PyTorch, Keras).
Experience with R, git, Hadoop, Cassandra, MapReduce, and AWS.
Also a plus:
You have in depth experience of Knowledge Graph, Semantic Web and Ontology (Reasoning).
Experience with adversarial models within Deep Learning (especially Generative Adversarial Networks).
Contribution to major deep/machine learning open source libraries.
Experience working in an Agile environment, CSD, CSM, SA, ASE
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