June 2, 2021

In this video our data analytics leader for international markets Alan O’Loughlin describes what it’s like to work in the field of data science, applying data analytics to insurance and the risk profession. He describes here a typical ‘day in the life of a data scientist’.

To build a career in data science takes some of the obvious skills such as mathematics and statistics, model building, an interest in problem solving, picking the right tools for a problem, focusing on experiment design and product analytics. But some of the required ‘soft skills’ are less obvious, such as the ability to network with others and drive change in the organisation, the ability to work across departments in making models that have the optimum business impact, and to be able to communicate to non-specialists around data modelling and data management.

At LexisNexis Risk Solutions we have an annual intake into our graduate recruitment programmes in the UK & Ireland, in the US and other countries, and we are playing a role in the up-skilling and teaching of data science.

A data scientist needs to be familiar with building and testing a predictive model, working with data, the structure of data. There’s a wealth of open source data out there already for any young person to experiment with and learn about critical thinking. Learn to prioritise effectively, to properly estimate how long tasks will take, think about your critical path to solving a problem, learn about the domain knowledge or business knowledge – the context of the problem – and then pretty soon you’ll become comfortable partnering with more experienced data scientists, talking about data science problems.

Watch as Alan O’Loughlin, Director, Data Science International (UKI, EU, Brazil and India) for LexisNexis Risk Solutions, gives some practical advice for a young person starting out, or considering a career in data science.

Follow the link for more information on how we support UK insurance.

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