Machine Learning Expert

• Integration and maintenance of software applications or services in the field of Artificial Intelligence (AI) in different technologies such as Natural Language Processing (NLP), Machine Learning (ML), Information Extraction, Knowledge Graphs (KG) or Graph Data Science (GDS).
• Perform market studies and technical evaluation of advanced commercial or open-source relevant products or services.
• Training of custom machine learning / deep learning models based on structured and unstructured data.
• Selecting features, building and optimizing classifiers using machine learning techniques.
• Interact with policy officers, data stewards and other stakeholders to understand business and data needs, turn them into use cases, prototypes or statements of work and eventually support the solution acceptance process.
• Define data controls and implement actions to ensure data quality, integrity and performance.
• Data mining using state-of-the-art methods.
• Design the IT architecture for solutions in the NLP / ML / AI fields, and coordinate its implementation considering master- and meta-data management concepts and security.

Requirements

• Master Degree in IT / Computer Science / Engineering or equivalent with specialisation in artificial intelligence and 17 years professional experience in IT.
• At least 7 years of specific expertise in Perl, Python, Matlab, R and their NLP/ML libraries (SpaCy, NLTK, scikit-learn, pandas).
• At least 7 years of experience with machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Neural Network, and/or artificial intelligence frameworks.
• At least 7 years of specific expertise in query languages, such as SQL, Hive, Pig, etc and with information extraction.
• At least 2 years of experience with AWS, Azure or other cloud platforms.
• Extensive experience with NoSQL databases, such as MongoDB, Cassandra, HBase, Neo4j, etc.
• Experience with data analytics over big datasets, non-structured databases as well as data lakes, including data visualisation tools (e.g. D3.js, knowledge graphs, etc.)
• Good knowledge of applied statistics, such as distributions, statistical testing, regression, etc.
• Fluency in English, optionally French will be considered an advantage.