Article Eleanor Brodie, data science manager, Insurance at LexisNexis Risk Solutions U.K. and Ireland

Competition for good data scientists, especially within the insurance industry, is currently hotter than ever and it is likely that demand will exceed supply for quite some time.

The past few years has generated a big shift in consumer behaviour and insurance providers are looking for new data driven solutions to better understand their customers. There has never been a better time for a woman interested in a career in data science to seek out the growing opportunities that exist within insurance.

Those with the core skills will be able to choose from the employers that offer the best opportunities beyond salary and benefits.  They will want to see how their ambitions for a career in data can be realised, that their job will be fulfilling and at times fun.  Creating the right infrastructure to both attract and retain this talent is therefore becoming a business imperative for insurance providers and data providers such as LexisNexis Risk Solutions, serving the insurance industry.

LexisNexis Risk Solutions employs data scientists across multiple insurance sectors – from looking at the risk of motor policy cancellations to analysing data based on a vehicle’s safety features or precisely mapping flood risk for the property insurance market.

As the insurance industry continues to evolve there is a gradual increase in new data sources available, such as satellite and aerial imagery to assess home and commercial property claims. There is a big interest in gaining access to accurate claims data gathered from across the market to help insurance providers improve the efficacy of pricing, underwriting and claims processing. The more data that comes into the market essentially means more opportunities for data scientists. Our data scientists work with billions of records to solve customer problems. Other companies can be limited in the breadth and depth of their data, but we are able to pull it all together in a commercially viable application.

But as with any relatively new role within the technology industry there are several misconceptions about what a data scientist really does. One of the biggest misconceptions is that big data and analytics will eventually replace human capital; this simply isn’t true. Anyone looking at a potential career as a data scientist should not underestimate the potential that human interaction with the data creates.

Another misunderstanding about data science that we encounter within the insurance sector is that there is a simple formula, where all data is poured into a magic funnel that draws out the desired outcome. Before any predictive model can be built the data needs to be enriched, filtered and structured correctly which is a process that relies heavily on quality data sources and knowledge of modelling.

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Looking to the future

If you are starting out in data science within any business, perhaps as a graduate, check what kind of journey you will be on from day one.  The LexisNexis Risk Solutions Data Science Rotational Program (DSRP) sees graduates from disciplines such as mathematics, statistics, computer science, data science, physics, financial math, actuarial science and engineering join LexisNexis for a two-year cycle through four different teams. This experience provides a robust hands-on journey from data access, data analysis, model building to model implementation.

Alternatively, if you are already within a business and want to move into a data science team, find ways to demonstrate you have a passion for data and a basic understanding of the mathematical concepts behind modelling.

Finally, look for organisations or teams that have female leadership groups that play a key role in attracting women to the business. If you can see that there are women at the top who are encouraging, mentoring and supporting other women in the business to reach their true potential, you will know you are on firm ground.

Part and parcel of nurturing talent is continuous on-the-job learning. We sponsor several additional education programmes and encourage employees to push themselves to achieve more, such as passing their actuarial exams.

My sign off piece of advice to a woman considering data science as a career is to be honest!  Data Science isn’t as sexy as it seems; yes, you can build a lot of cool stuff but to build the cool stuff that actually works in the real world you have to understand the data. It’s a hard-fought battle to properly understand data and build something that effectively better predicts an outcome or deploying automation. You must move from the safety of an R&D environment to a production environment, which can be a scary prospect and a hard road to get all the pieces to align.  If you love digging into the data, analysing it and helping companies find insights or even better, do good for society, then you’ve found the right spot.