European Institutions:data Scientist
Il y a 5 mois
**WHO WE ARE**
- **Etinars** is a values-focused company with multi-year experience, specialised in the **recruitment of professionals** for niche markets - managing **the full-life cycle** of specialist and executive level hires.
- At Etinars, we genuinely care about **who you are and what you need**.
- We place great emphasis on fostering robust, enduring **connections** built upon **trust and transparency**.
- Our approach ensures a **swift journey**, leading you hand in hand towards **signing** your next career step.
**WHAT WE ARE LOOKING FOR**
**_European Institutions: Data Scientist - AI/ML/NLP_**
Join our client’s team in Brussels as Data Scientist - AI/ML/NLP within the European Commission.
The mission of DIGIT.A.4 is to deliver digital reusable solutions and mobile solutions for the European Commission as well as for citizens in the Member States in cooperation with other Commission services.
One of the reusable solutions provided by DIGIT.A.4 is the Corporate Search, a service that allows users and/or systems to perform "cross-domain" and "cross-platform" searches. In order to do so, the service provides several interfaces that allow users and systems to retrieve relevant information about any topic, as well as needed integrations in order to index or federate information resources.
The solution assignment may change over time, according to the portfolio/project/service needs.
**YOUR TASKS**
- Training of custom machine learning / deep learning models based on structured and unstructured data;
- Selecting features, building and optimizing classifiers using machine learning techniques;
- Studies and developments aiming at improving the quality of machine translation (MT) engines for each installed language pair, addressing the specific needs of customers of the service concerning MT quality and contributing to a general strategy for the systematic evaluation and long-term improvement of MT quality;
- Interact with data stewards and other IT stakeholders to define the data rules;
- Define data controls and implement actions to ensure data quality and integrity;
- Creating automated anomaly detection systems and constant tracking of its performance;
- Data mining using state-of-the-art methods;
- Processing, cleansing, and verifying the integrity of data used for analysis;
- Design the IT architecture for solitons in the NLP / ML / AI fields, and coordinate its implementation considering master
- and meta-data management concepts;
- Analyse data architecture for consistency, completeness, accuracy and reasonableness;
- Contributing for the analysis of data management vision, strategy and policy and derive the IT requirements;
- Contributing to the design of the IT architecture considering master
- and meta-data management concepts;
- Elaboration of test programs;
- Writing of technical documentation;
- Assistance with deployment and configuration of the system;
- Participation in meetings with the contracting authority, project teams and user groups.
**YOUR SKILLS AND EXPERIENCE**
Following skills and knowledge are required:
- Knowledge of the involved Mathematics and Statistics (Linear algebra, Calculus, Probability theory,Statistics, Optimization techniques, etc.).
- Very good knowledge of programming in Python (programming in R is considered an asset).
- Very good experience with libraries and frameworks like TensorFlow, PyTorch, scikit-learn, and Keras.
- Extensive hands-on experience with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
- Good knowledge of deep learning and neural networks.
- Good knowledge of data pre-processing and analysis techniques.
- Good experience with data visualization using tools like Matplotlib, Seaborn or Plotly.
- Good experience with RDBMS like Oracle (latest version).
- Good understanding of NoSQL databases like MongoDB or Cassandra.
- Extensive hands-on experience with using popular ML frameworks and libraries like TensorFlow and PyTorch.
- Experience in training deep learning models.
- Good knowledge of NLP techniques and libraries like NLTK, spaCy, or Transformers.
- Knowledge of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.
- Extensive experience with model evaluation and validation techniques.
- Extensive experience with deploying ML models to production.
- Good understanding of ethical considerations in AI/ML.
- Good knowledge of potential bias and fairness issues in AI systems.
- Familiarity with reinforcement learning algorithms and environments.
- Knowledge in using cloud platforms like AWS, Azure, or Google Cloud for scalable AI/ML solutions.
- Familiarity with distributed computing frameworks such as Apache Spark or Hadoop for handling large dataset will be considered an asset.
- Ability to give business and technical presentations.
- Ability to cope with fast changing technologies used in the domain of Artificial Intelligence.
- Good communicatio
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