Career Compass

In the evolving landscape of reinsurance, the role of data science has become increasingly pivotal. Data science allows reinsurers to analyze vast amounts of data from various sources, leading to more accurate risk assessment as well as innovative (re)insurance products and services that benefit consumers and businesses alike. This new discipline encompasses various specializations, from data analysis to data visualization and modeling, making it a multifaceted profession.

For example, at SCOR, our data scientists have created solutions that improve our internal processes by extracting key information from our documents, using Artificial Intelligence systems that understand the language specific to insurance. We have currently processed more than 100,000 documents with this automated system. We have also used machine learning models to better understand our risk models and developed dashboards for an easier understanding of our data.

Roberto Castellini, the Head of Data Science L&H at SCOR, answered our questions on the role of data scientists in reinsurance and his advice for anyone interested in becoming a data scientist in the reinsurance industry.

Can you explain what a data scientist does in simple terms?

A data scientist’s role is to handle everything related to data, from its raw format to its visualization and modeling. Data scientists operate at the intersection of statistics, mathematics, computer science, and business knowledge. This unique blend of skills allows them to extract meaningful insights from complex datasets, which is crucial for making informed decisions in reinsurance.

What educational background is typically required to become a data scientist?

There exist many routes to becoming a data scientist. The two main roads are studying statistics or computer science. Alternatively, studying other disciplines with quantitative approaches such as mathematics, physics, or biology can allow you to enter the field of data science as well. Even with a more business-related background, becoming a data scientist is possible if one is skilled in analytics, statistics, or computer science.

What skills and qualifications are essential for a data scientist?

To excel as a data scientist, one needs a combination of hard and soft skills. On the technical side, proficiency in coding and a strong foundation in statistics and mathematics are essential, as already mentioned. Depending on the specific role, some data scientists may focus more on business aspects, while others specialize in statistical analysis. 
Soft skills are equally important. Effective communication, teamwork, and the ability to understand and meet the needs of various stakeholders are crucial. Data scientists must be adept at explaining complex concepts in simple terms and working collaboratively with other teams to provide valuable insights.

How would you describe the role of Data Scientists in Reinsurance?

In the reinsurance industry, data scientists play a vital role in analyzing portfolios or customer data, developing statistical models, and improving internal processes. To effectively analyze and interpret data, a deep understanding of the industry is needed. Overall, the work of data scientists aims to create value for the consumer. 
Typical tasks of a data scientist at SCOR involve taking raw data, cleaning and analyzing it, and testing different models to derive actionable insights. Lastly, coding the full solution is also part of their work. This process is crucial for enhancing risk assessment, optimizing operations, and driving strategic decision-making. 
For example, one of my recent projects was to extract key information from documents to improve our internal processes. We had to understand which key information we needed to extract, mostly by consulting with technical experts. Then we extracted that information from our internal systems when possible or manually labelled the dataset. Finally, we trained our machine learning models and evaluated their performances, before deploying the model to production.

How do data scientists at SCOR collaborate with other departments and how do they overcome some common challenges?

Data scientists at SCOR often collaborate with various departments such as Underwriting or IT teams. They provide advice, suggest approaches, and work on use cases that require their expertise. One of the biggest challenges we face is changing the mentality around modeling and predictions. It’s essential to communicate the limitations of models and manage expectations effectively.

How has the role of data science evolved with advancements in technology and how do you see the role of data scientists in reinsurance develop in the future?

While technical skills remain important, understanding data and effective communication have become even more critical. In the future, technical tools for model development and coding will save data scientists time on that front. Therefore, there will be a continuous shift in importance towards being able to understand data correctly and communicating about it effectively.

What are the potential growth opportunities and career paths for a data scientist within SCOR?

At SCOR, data scientists have significant growth opportunities due to the centrality of data in the company’s Forward 2026 strategic plan. 
Firstly, data scientists are encouraged to develop new projects and work closely with their team. SCOR offers a variety of internal and external training programs to enhance skills. The goal is to help employees identify their niche and specialize in areas such as statistics, computer science, or domain-specific knowledge. 
Secondly, skill development among non-data scientists is promoted as well. For this, SCOR identifies certain employees as “data natives,” providing them with tools and support to undertake data science projects. This approach allows non-experts to apply data science techniques in their roles, fostering a collaborative environment where data science skills are widely disseminated.

What advice would you give for those looking to enter the field of data science in the reinsurance industry?

Being a data scientist in reinsurance means leveraging data to enhance risk assessment, optimize operations, and drive strategic decisions. It’s a role that requires a unique blend of technical and soft skills, continuous learning, and the ability to adapt to an ever-changing landscape.

Roberto started his career in Academia with a Ph.D. in Mathematics. He then continued his professional path as a Data Scientist, working in a robotic company and in an Insurtech startup. He joined SCOR in 2020 as a Data Scientist and now holds the position of Head of Data Science for Life & Health.

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