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AI Engineer with less than a year in Data Science, Python, and Java.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Étudiant en première année du cycle ingénieur spécialisé en Sciences des Données, Big Data et Intelligence Artificielle, motivé et rigoureux, avec de solides bases scientifiques acquises en classes préparatoires MPSI. Intéressé par le développement informatique, l'analyse de données et les nouvelles technologies.
École Nationale Supérieure de l'Intelligence Artificielle et Sciences des Données
Cycle Ingénieur · Sciences des Données, Big Data & Intelligence Artificielle
August 1, 2025 – June 30, 2026
Classes Préparatoires aux Grandes Écoles (CPGE)
Classes Préparatoires · Filière MPSI
August 1, 2023 – June 30, 2025
Unknown
Baccalauréat · Sciences Physiques
June 1, 2022 – May 31, 2023
Conception d'un projet théorique sur la conversion de l'énergie thermique en énergie électrique
June 29, 2026 – Present
Incluant l'étude des principes thermoélectriques et de l'efficacité énergétique.
Cultural Fit Analysis
The candidate's academic background and stated interests align with a learning-oriented and technically driven culture. The project diversity, though limited to academic contexts, shows an interest in applying technical skills to real-world problems (e.g., flood monitoring). The pursuit of an AI-focused engineering degree suggests a strong alignment with roles requiring continuous learning and innovation in AI/Data Science. However, the lack of professional experience or diverse project types limits a deeper assessment of cultural fit beyond academic alignment.
Soft Skills & Operational Fit
The candidate lists several soft skills such as teamwork, adaptability, organizational skills, priority management, and adherence to instructions/deadlines. These indicate a potentially good operational fit for structured environments and collaborative teams. However, these are self-declared and not validated by assessments or work experience.