Data Scientist

Descartes Underwriting is seeking a Data Scientist to join their team in Puteaux, France. The role involves developing climate models and forecasting tools, improving algorithms and risk models for B2B clients, and collaborating with various teams. Candidates will work autonomously and may take on management responsibilities. The company focuses on data-driven insurance products to address climate risks and has an international presence with a commitment to innovation.
Company Description

WHO WE ARE | Founded in 2018 and headquartered in Paris, Descartes Underwriting was born of the conviction that climate change calls for a revolution in the insurance industry to better protect individuals, businesses, and governments. We provide data-driven insurance products based on satellite imagery and IoT to protect corporate and public sector clients against climate risk and natural catastrophes. Following a Series A funding of 18m USD in September 2020, we opened offices in New York, Houston, Singapore, Sydney and London. Today, Descartes’ ambitious and international team of 40+ is proud to protect 100+ clients – several from the Fortune 500 – in 60+ countries.

OUR PURPOSE | Losses from catastrophes and man-made disasters are likely to increase year after year due to demographic density and climate change. We firmly believe that the insurance sector can and must play a critical role in helping enterprises and individuals cope with climate risk. This will require, however, a deep transformation of the sector, as current insurance products often lack transparency, carry expensive premiums, and come with slow claims processing.

OUR APPROACH | True to our namesake, René Descartes, one of the greatest European scientific philosophers of the 17th Century, our company draws a distinct advantage in challenging the approach adopted by traditional insurers. Utilizing forward-looking models and real-time monitoring from satellite imagery, IoT and big data analytics, Descartes’ products help businesses bounce back faster. In collaboration with our partners, our parametric insurance products can serve clients across the globe, from mid-size tickets to EUR +100 million.

Job Description

Due to rapid growth we are seeking to expand our Data Science team, either across our Underwriting or our R&D functions. At the core of our company’s strategy, your missions will focus on making direct contributions to the development of new climate models or forecasting tools.

KEY MISSIONS

If you join the underwriting team, your key missions will include:
• Improving or developing new algorithms, new risk models and products for our B2B clients
• Identifying, implementing and deploying new statistical and machine learning methods to differentiate Descartes from its competitors
• Participating in the development of Descartes’ technological platform
• Collaborating with the business team to understand client needs and issues to further strengthen our technical excellence
• Taking on management responsibilities as both you and the company develop
• Participating in our R&D sessions
• Working autonomously and pragmatically to make appropriate technical decisions

If you join the R&D team, your key missions will include:
• Improving or developing new models to improve our understanding of climate risks with a special focus on wildfires or floods
• Conducting research on the modelling of climate risks and machine learning methods
• Identifying new and innovative ideas and testing state-of-the-art methods from scratch based on recent research articles
• Collaborating with the underwriting and business teams to ensure our products are efficiently integrated into the underwriting process and meet our client’s needs
• Working autonomously and pragmatically to make appropriate technical decisions

Qualifications

EXPERIENCE & SKILLS

Underwriting Data Scientist
• Masters student in computer science, applied mathematics, statistics or meteorological studies
• Ideally a previous experience (long-term internship) in data science or climate modelling
• Proficient in statistics, applied mathematics and machine learning methods
• Capable of building high-performance algorithms
• Proficiency in Python (e.g. scikit-learn)

R&D Data Scientist
• Research-oriented Master’s Degree or PhD in physics, applied mathematics, or meteorological studies
• Multi-disciplinary curriculum, with strong skills in statistics, applied mathematics and machine learning methods
• Ideally a previous experience in climate modelling or data science
• Proficiency in Python

LANGUAGES
• Fluency in English (written and verbal communication) required
• Good command of one additional language (e.g. Chinese, French, Italian, German, Spanish…) valued

MINDSET
• Interested in weather and natural perils modelling (wildfires, hail, tsunamis, earthquakes etc)
• Strong team spirit and ability to work under pressure
• Highly motivated, able to meet deadlines set
• Strong desire to learn and commitment to the organization’s mission
• Results oriented, high energy, with the ability to work in a dynamic and multi-cultural environment
• Motivated to help improving businesses’ and communities’ resilience to climate change
• Eagerness to work in an international environment

Additional Information

WHY JOIN DESCARTES UNDERWRITING?
• Opportunity to work and learn with top data scientists from the most prestigious schools and research labs in France, allowing you to progress towards technical excellence
• Commitment from Descartes to its staff of continued learning and development (think annual seminars, training etc.)
• Work in a collaborative & professional environment
• Be part of an international team, passionate about diversity
• Benefit from a referral scheme for successfully referring peers

RECRUITMENT PROCESS

Step 1: Call with our Talent Recruiter, Aicha

Step 2: Technical online test

Step 3: In-person interview with a Data Scientist

Step 4: In-person interview with Head of our R&D or Underwriting

Step 5: Final round interview with our CEO & Data science team (this stage is for full time roles only)