Senior 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 an understanding of L&H actuarial technics (Experience Analysis, survival modelling) and embed them into relevant modelling approach.
- Have a high degree of autonomy when developing models and determining the appropriateness of a given approach
- Help driving innovation in the underwriting process through close collaboration with different parties including the client, underwriters, and actuaries.
- Own topics of priority to the team and deliver on-time to agreed quality standards
- Be a key contributor to all predictive UW and claims related projects supporting all regions and internal/external clients.
- Support strategic innovation initiatives globally to transform process (e.g. underwriting, claims) from a machine learning perspective.
- Proactively identify relevant R&D for business needs
- Collaborate with SCOR's thriving global AI community by being a key contributor on research projects
- Increase the interpretability of models through advanced understanding of AI and machine learning
- Present results to stakeholders and explain complex topics clearly using suitable interpretation methods for clients.
- As a member of the Data Science chapter, the Senior 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
- ~3-5 years' experience in data science with strong programming capabilities and advanced knowledge of supervised and unsupervised machine learning techniques
- Insurance industry experience is required
- Can perform code peer reviews and merge requests
- Strong critical thinking skills and ability to learn quickly
- Good technical expertise on cloud computing platforms such as AWS and Microsoft Azure (or sufficient basics to learn fast the usage of cloud technologies)
- Expert knowledge of Python
- Experience using machine learning to develop high-quality and practical solutions
- Deep understanding of predictive modeling concepts, machine-learning approaches, clustering, classification and crowdsourcing techniques (e.g GLMs, Decision Trees, SVM, Random Forests, GBM, PCA, Bayesian Networks, Neural Networks, etc.) applied to L&H
- Ability to communicate, educate, and advise colleagues and clients on predictive modeling concepts, machine-learning approaches, clustering, classification and crowdsourcing techniques (e.g GLMs, Decision Trees, SVM, Random Forests, GBM, PCA, Bayesian Networks, Neural Networks, etc.)
- Ability to adapt communication style to the level of technical expertise of the audience
- 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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