Data Scientist Internship (6 months)

Within Quinten Health, you will support evidence and data-driven insights generation for optimal decision making and maximal business, medical or public health impact, through the following tasks: • Operating on the framing of the project and participate in the translation of customer needs into data issues; • Participating in the design and implementation of the solution to be provided: data structuring, modeling, interpretation of the results; • Participating in exchanges with the client as well as in the realization of the supports and deliverables of the project; • Participating in scientific and technological watch. Preferred experience Preferred experience Coming from an engineering school and / or holding a master’s degree in data science, you are looking for an internship and ideally have a 1st experience on a data science project (internships, study projects, …). You have the following skills: • Python programming; • Good knowledge of statistical and machine learning techniques; • Good knowledge of machine learning libraries for Datascience (e.g., pandas, NumPy, scikit-learn, XGBoost, SHAP…); • You are really interested in the Healthcare sector; • You have synthesis and communication skills allowing you to argue technological choices and explain their risks and benefits; • You have qualities of organization and rigor, and the taste for teamwork. Additional Skills • Knowledge of continuous integration (Git versioning, unit tests); • Knowledge of distributed computing (parallelization and/or Spark); • R programming; • 1st experience in Healthcare sector would be a plus. Quinten Health recruits based on skills, qualifications, and motivation. All applications are considered fairly and evaluated on an equal basis. If you require any accommodation during the application or interview process, please let us know. We are committed to providing the support needed to ensure an accessible and inclusive recruitment experience . Recruitment process 1. A meeting with HR manager. 2. A use case and a meeting with a senior data scientist. 3. A business case with a project manager 4. A meeting with a Data Scientist manager.