Core Data Scientist

SCOR; Paris; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

Une entreprise du secteur de l'assurance recherche un Data Scientist pour intégrer son équipe dynamique. Le candidat idéal a 1 à 3 ans d'expérience en sciences des données, avec une solide maîtrise de Python et des techniques d'apprentissage machine. Les responsabilités comprennent le développement de modèles AI et la collaboration avec divers acteurs pour soutenir l'innovation dans l'assurance. Une éducation pertinente en STEM est requise.

Qualifications

• 1-3 ans d'expérience en data science avec une solide capacité en programmation.
• Connaissance des techniques d'apprentissage supervisé et non supervisé.
• Diplôme de Master requis, un doctorat est un plus.

Responsabilités

• Développer des modèles statistiques et d'apprentissage machine avancés.
• Collaborer étroitement avec des experts métiers et des clients.
• Communiquer des résultats et former des connaissances en data science.

Connaissances

Python

Machine Learning

Statistics

Data Protection Compliance

Formation

Master’s degree in Science, Technology, Engineering, Mathematics, or similar

Outils

Pandas

Scikit-Learn

Docker

Description du poste

We are seeking a Core Data Scientist to deliverGenAI and machine learning models that align with our broader business & AI strategy. As part of a cross-functional delivery team, you work directly with business experts and SCOR clients, developing a strong understanding of their needs and build impactful AI models, in line with best practices on lean product delivery. You are part of the Data & Analytics Office, which drives the strategy, execution and governance of SCORs AI ambition, working in a global team of AI and data experts on some of SCORs (and SCOR’s clients) most important challenges and opportunities.

Key duties and responsibilities

Approach

• Develop advanced statistical, predictive, or machine learning models using deep knowledge of the algorithms and hyperparameters and systematically applying coding best practices.
• Have a high degree of autonomy when developing models and determining the appropriateness of a given approach

Projects

• Being a hands-on and active doer in the delivery of projects
• Help driving innovation in insurance areas through close collaboration with different parties including the client, underwriters, and actuaries.
• Contribute to key topics of priority to the team and deliver on-time to agreed quality standards
• Be a key contributor to regional market projects as first priority, but also a core contributor on global projects including OCR, NLP, Gen AI, visualization, templates, etc.
• Support strategic innovation initiatives globally to transform process (e.g. underwriting) from a machine learning perspective.
• Proactively identify relevant R&D for business needs
• Be able to conduct research spikes to solve technical challenge
• Collaborate with SCOR’s thriving global data analytics community by being a key contributor on research projects and communication

Communication

• Increase the interpretability of models through advanced understanding of artificial intelligence and machine learning
• Present results to stakeholders; clearly communicate complex topics by applying appropriate interpretation techniques and visualizes these for the benefit of internal / external clients
• As a member of the Data Science chapter, the Core Data Scientist will be an ambassador of the existing chapter and contribute to it (participating to training, maintain a certain level of knowledge by getting training as well on advance topics and developing skills) : Be a key distributor of knowledge within SCOR globally
• Spread data science knowledge externally through seminars and publications

Compliance

• Adhere to all Information Security policies and best practices, including security awareness training and other information protection initiatives
• Be fully compliant with GDPR and other local data protection legislation
• Be aware of regulatory and reputational risk when developing consumer-facing AI tools and suggest ways of mitigating these

Required experience & competencies

• 1-3 years’ experience in data science with solid programming capabilities and knowledge of supervised and unsupervised machine learning techniques
• Insurance industry experience is preferred, but not required
• Strong knowledge in statistics and basic models : mathematics (probability) + usage of libraries (sklearn, pandas)
• Uses Python in an advanced way (~go beyond notebooks, produce scripts, modules, POO, packaging)
• Seek for answers by themselves by knowing the key concepts to look at (debugging code, google right terms, looking for proper help)
• Keeps up to date on academic research where relevant to business needs (reads ML / stats papers)
• Is able to industrialize ML models (e.g., git usage, basics on Docker) - or can quickly learn (~1 / 2 sprints)
• Understands and follows relevant data protection laws and best practice
• Being able to familiarize with new programming tools

Required Education

• Master’s degree (Ph. D. is a plus) in Science, Technology, Engineering, Mathematics, Computer Science, Actuarial or similar quantitative field
• Bachelor’s degree plus ASA or similar work experience is accepted in place of a relevant Master’s degree.