Core AI Specialist

Job Summary: We are seeking a Core AI Specialist to deliver GenAI 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 AI and Advanced Analytics team, 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.

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.
• Structure thoughts and guidelines in advance way when using agentic tools - being in capacity to challenge intermediate results, robustness and to manual reproduce specific outcomes for proper validation.

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.

R&D

• 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 AI Specialist 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 AI and 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.

Preferred Qualifications:

• 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.
• ~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).
• Capacity to use efficiently AI Agentic assistant in coding (setting skills, calibrating system prompts) · Usage of Chain of Thoughts, prompting, and orchestration.
• 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 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.
• <