Missions

• Support the business activities in APAC region.
• 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.

• Being and hands-on and active doers in the delivery of projects
• Help drive 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, visualization, templates, etc.
• Support strategic innovation initiatives for the Americas markets 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

• 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

• 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

Profil recherché

• ~1-3 years’ experience in data science with solid programming capabilities and knowledge of supervised and unsupervised machine learning techniques
• 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
• Insurance industry experience is preferred, but not required

• Shares and communicates about his/her work to rest of the technical team with accurate terms
• Documents his/her work (be able to write a technical report with explicit relevant and self-explicit charts, follow templates, etc.)
• High level controls on his/her work
• Proactively supports other team members with technical help and adopts a team mindset
• Is realistic with timeframes and updates relevant stakeholders on progress
• Follows some quality standard when presenting / documenting / communicating
• Fluence in Chinese is preferred but NOT required

• Proactively identifies and raises technical concerns/doubts on data projects
• Understands instructions and contributes to the vision by questioning or enriching the tasks defined during a project

• 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.