Data Scientist/Machine Learning Engineer — NLP

15 августа 2019    50
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3+ years professional experience as a data scientist using advanced data analytics techniques. Experience natural language processing, natural language understanding, machine learning, deep learning, algorithm development.
Experience with NLP packages such as SpaCy, CoreNLP, OpenNLP, NLTK, scikit-learn
Experience building data pipelines and deploying models into production
Master’s Degree in a quantitative discipline such as Applied Statistics, Mathematics, Computer Science, Engineering, Physics, or equivalent.
Experience using tools such as Python, R, and SQL for data analytics with large data sets including structured and unstructured.
Knowledge of Machine learning in theory and in practice. Experience working with machine learning models from development through testing, validation, and production.
Excellent interpersonal and English verbal and written communication skills
Ability to explain the process and analysis results to both technical and non-technical audiences.

Experience with deep learning for NLP/NLG
Experience with big data tools and platforms (Spark)
Experience working with one or more cloud platforms such as AWS, Google Cloud, and Microsoft Azure
Active in the data science or technology community (meetups, blogs, competitions, etc.)
Experience working on projects for US or European clients
Familiarity with business use cases of machine learning for building NLP products

Remote position with flexible work hours. May be part time or full time. Competitive compensation based on work experience with potential for bonuses based on results. Fast paced low bureaucracy environment with ability to experiment with new approaches and emerging technologies. Exposure to a variety of interesting cutting-edge projects. The opportunity to join a rapidly growing company with career progression and opportunities.

Apply the latest advances in data science research in NLP and incorporate it into practical product features
Learn new domains and problems.
Experiment with models and data.
Present results to technical and non-technical stakeholders
Collaborate with other experts

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