Climate Risk Data Scientist - Parametric Insurance

Orakl Oncology; Paris; Hybride; EUR 17 000 - 23 000; Temps partiel

Avantages offerts par ce poste

Competitive salary

Home office days

International team

Professional development

Résumé du poste

Descartes Underwriting in Paris is expanding its data science team within Underwriting and seeks Data Scientist (H/F) to contribute to climate models and forecasting tools. You will work with cross-functional teams to build robust models for parametric insurance and public sector clients worldwide.

Master’s students in data science or related fields are encouraged to apply; experience in climate modeling valued.

Qualifications

• Master’s student in data science, computer science, applied mathematics, statistics or meteorological studies or related.
• Ideally a previous experience (long-term internship) in data science or climate modeling is valued.
• Fluency in English and ability to communicate across teams is required.

Responsabilités

• Improve or develop algorithms, new risk models and products for our B2B clients.
• Identify and deploy statistical and ML methods to differentiate Descartes.
• Contribute to the development of Descartes’ technological platform.
• Collaborate with business teams to understand client needs and issues.
• Take on management responsibilities as the company grows.
• Work autonomously to make appropriate technical decisions.

Connaissances

Statistics

Applied mathematics

Machine learning

English fluency

Additional language

Formation

Master’s student in data science / CS / math / statistics / meteorology

Description du poste

Descartes Underwriting in Paris is expanding its data science team within Underwriting and seeks Data Scientist (H/F) to contribute to climate models and forecasting tools. You will work with cross-functional teams to build robust models for parametric insurance and public sector clients worldwide.

Master’s students in data science or related fields are encouraged to apply; experience in climate modeling valued.