Core Data Scientist - Beijing
SCOR
- Beijing
- Permanent
- Full-time
- 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 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 Chinese and Asia-Pacific market projects as a priority, but also a core contributor on global projects including OCR, NLP, visualization, templates, etc.
- Support strategic innovation initiatives to transform processes (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 all relevant local data protection legislation.
- Be aware of regulatory and reputational risk when developing consumer-facing AI tools and suggest ways of mitigating these.
- Minimum 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)
- High degree of technical expertise on cloud computing platforms such as AWS and Microsoft Azure - Alibaba cloud expertise is a plus. At least a strong interest to learn about those technologies is required.
- Understands and follows relevant data protection laws and best practice.
- Able to familiarize with new programming tools.
- Experience with database query tools such as SQL is preferred, but not required.
- Insurance industry experience is preferred, but not required.
- Proficiency in Chinese is a MUST as you are based in the Beijing Office.
- Master's degree in science, Technology, Engineering, Mathematics, Computer Science, Actuarial or similar quantitative field
- Actuarial exam progress 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.
- 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.