Xnext
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Data Scientist

Role description

Xnext is a young and booming company, that has developed an innovative technology for quality control based on a multispectral X-ray analysis.

We are looking for a Data Scientist to develop algorithms for our inspection software.


Your tasks

  • Workflow from the data creation until the neural network model;
  • Processing of multispectral images stemming from the X-ray inspection;
  • Developing innovative algorithms based on mathematical methodes;
  • Implementation of neural networks and evaluation of their performances;
  • Broadening of our proprietary library;
  • Liaising and cooperating actively with other teams in order to maintain an effective alignment;
  • Production of reports for internal and external customers.

Your Profile

  • Brilliant graduate in Mathematics, Physics, Mathematical or Physics Engineering, Data Science or Statistics;
  • Ability to use typical Deep Learning architectures;
  • Good knowledge in programming, especially in Python (libraries such as Keras, Scikit-learn, TensorFlow and NumPy);
  • Knowledge of data processing and image analysis methodologies;
  • Familiarity with statistical methods for data analysis (classification or anomaly detection algorithms, and clustering);
  • A previous experience in Computer Vision algorithms development would be appreciated.

Your Data Scientist profile must be completed with:

  • Flexibility, proactivity and stress resilience;
  • Excellent team working skills;
  • The ability to think “outside of the box”;
  • Excellent priorities, deadlines and timelines management;
  • Results oriented;
  • Effective communication;
  • Fluency in English

Why you’ll love working at Xnext®

The selected candidate, who will work in a highly technological, innovative and dynamic environment, will be offered a competitive salary, commensurate with the actual experience gained.


Apply now

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    Xnext Eu fundend No 849381

    Xnext has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement N° 849381