ABOUT DESCARTES UNDERWRITING

Descartes was born out of the conviction that the ever-increasing complexity of risks faced by corporations, governments and vulnerable communities calls for a renewed approach in insurance. Our team brings together industry veterans from the most renowned institutions (AXA, SCOR, Swiss Re, Marsh, Aon, ...) and scientists on top of their field to bring underwriting excellence. After 5 years of existence, Descartes has secured a leading position in parametric insurance for weather and climate-related risks utilizing machine learning, real-time monitoring from satellite imagery & IoT. After a successful Series B raise of $120M USD, we launched Descartes Insurance, a 'full stack' insurer licensed to underwrite risk by the French regulator ACPR. With a growing corporate client base (350+ and counting), our diverse team is headquartered in Paris and operates out of our 13 global offices in North America, Europe, Australia, Singapore, Hong Kong and Japan. Descartes is trusted by a panel of A-rated (re)insurers to carry out its activities.

ABOUT YOUR ROLE

Due to our consistent growth, we are expanding our Data, Software and DevOps team. We are seeking profiles dedicated to data engineering. At the core of the development of our scientific engine modeling climate phenomena, your main missions will be to create, improve and maintain the data pipelines used to train our model and infer the different scenario to make a climate risk assessment. You will have to take initiative and assess the viability of proof of concept projects.

You will have to work with data scientists and software engineers to run and develop our models. You will be working along DevOps engineers to reliably put models in production and selected the compute/store instance needed to perform these tasks. Your secondary mission will be to automate the flow of information between the tech and business to monitor climate events.

🔔 KEY MISSIONS 🔔

• Setup, automate, maintain and update:
• Connections to external and internal APIs;
• Data preparation process;
• Model training and inference process;
• Data storage process;
• Associated CI/CD pipelines;
• Associated package versioning and releasing pipeline;
• Modularization of code base;
• Notification tools to inform the team of the status of the operations.

• Setup data storage, data processing and data visualizing tools, by :
• Assessing the pains and needs of the teams;
• Benchmarking the open source and private solutions;
• Assessing the security, price and reliability of data architecture;
• Following the development the evolution of technologies on the topic;
• Forecasting the usage of the tools;
• Tracking the cost of the tools.
  

• Participate in:

• Tech stack selection;
• Discussions with tech partners;
• Training of software and underwriting teams;
• Support and debug of internal users.
  
  

TECH STACK 🖥️

• Cloud provider: GCP
• Code versioning tool: Git + Gitlab
• OS: Linux
• Container: Docker
• Container orchestrator: Kubernetes
• Website architecture: LAMP
• Code base: Python
• Notification tool: Slack
  
  

DATA STACK 🗄️

• Types: images, timeseries,
• Storage: GCP bucket
• Version: DVC (roll out in progress)
• Pipeline: Airflow (PoC stage)
• Data base: to be setup depending on the use cases

In our project, data is collected by sensors (satellite, weather station, IoT). We don’t work with personal or sensitive data, in most cases the data is publicly available (earthquake magnitude, cyclone track, precipitation …).

ABOUT YOU 

EXPERIENCE & QUALIFICATIONS 💻🖥️‍💻

[Hard skills]

• Knowledge of the tech stack or equivalent tools;
• Experience converting python code to efficient data engineering tools (eg: spark);
• Experience with Docker;
• Experience with a cloud provider (GCP, AWS or azure);
• Experience automating a CI/CD pipeline;
• Good knowledge in English and fluency in French.

[Soft skills]

• Desire to train junior developers and explain CI/CD and cloud tools;
• Desire to suggest improvements to the architecture.

[Nice-to-have]

• Experience working data science project or scientific code;
• Experience with Kubernetes;
• Experience in HPC;
• Contribution to an open source project.
  

MINDSET 💥

• Strong interest with climate issue (it’s not a hoax, many people suffer from it);
• Being comfortable to work alongside corporate insurers (some still wear suits 👔);
• You enjoy CI/CD automation (or at least appreciate the elegance of a well-crafted pipeline);
• Strong team spirit and ability to work (you’ll have to review code and have your code reviewed);
• Rigorous, creative and meticulous mind (we handle large insurance, we take our time);
• Strong desire to learn (there’s no limitation to the tech used, we’re happy to test and learn new tools);
• Eagerness to work in a multi-cultural environment (policies and teams are from all around the world 🗺️).
  

WHY JOIN DESCARTES UNDERWRITING?

• Opportunity to work and learn with teams from the most prestigious schools and research labs in the world, 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 ;
• Join a company with a true purpose – help us help our clients be more resilient towards climate risks;
• A competitive salary, bonus and benefits;
• You can benefit from a punctual home office days.

At Descartes Underwriting, we cherish value of diversity whatever it may be. We are committed to fighting against all forms of discrimination and for equal opportunities. We foster an inclusive work environment that respects all differences.

With equal skills, all our positions are open to people with disabilities.

RECRUITMENT PROCESS

• Step 1: Call and HR Interview with our Talent Recruiter
• Step 2: Technical project submitted via GitHub
• Step 3: Technical interview
• Step 4: Manager interview
• Step 5: Final round interview with the team
  (Candidates can opt to have the manager interview before the technical project and interview)