Senior Data Scientist

Talent 4.0 BVBAIT services

Candidates need to be based in Europe.ContractSenior

Salary not disclosed

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Job Details

Languages: EU Candidates need to be fluent in English.
Experience: Master Degree in a related field with 11 years of professional experience in IT. +3 years of professional experience in the domain as a Data Scientist.
Required Skills: PythonArtificial IntelligenceMachine LearningMatlabNLTKPandasRscikit-learn

Hold a master’s degree in a related field.
Have 11 years of professional experience in IT and 3 years of professional experience as a Data Scientist.
Have experience in machine learning and natural language processing.
Have excellent knowledge of Perl, Python, Matlab, R, and NLP/ML libraries including spaCy, NLTK, scikit-learn, and pandas.
Have knowledge of SQL tooling and NoSQL databases, including MongoDB, Hadoop, Cassandra, or HBase.
Know query languages such as SQL, Hive, and Pig, and have experience with information extraction.
Know data visualization tools such as D3.js or GGplot.
Have good knowledge of AWS and/or Azure, Linux, Unix, and Bash.
Have excellent knowledge of machine learning techniques and algorithms such as k-NN, Naive Bayes, SVM, decision forests, and neural networks, and/or AI frameworks.
Have knowledge in predictive or prescriptive methods, sentiment analysis, topic detection, social media crawling and processing, plagiarism detection, anomaly or trend detection, or recommendation systems.
Have experience in corpus-based linguistics, alignment models, and classification methods.
Know NLP system lifecycles and agile software development methodologies.
EU candidates must be fluent in English.

Develop and maintain software applications in NLP, machine learning, and AI.
Train custom machine learning and deep learning models using structured and unstructured data.
Select features and build and optimize classifiers using machine learning techniques.
Follow developments to improve machine translation engines for installed language pairs.
Work with data stewards and IT stakeholders to define data rules.
Create automated anomaly detection systems and track their performance.
Mine, process, cleanse, and verify the integrity of data used for analysis.
Design IT architecture for NLP, ML, and AI solutions and coordinate implementation with master- and metadata-management concepts.

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