Core Data Scientist
- 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 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
- 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
- ~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
- 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.
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