Sourcing the insurance industry's key ingredient

Why are data science skills in demand?

Data science is an exciting, evolving and expanding field – very attractive for new entrants and vital for businesses in almost all sectors. A study by the Royal Society earlier this year found that demand for workers with specialist data skills - like data scientists and data engineers - has increased over five years (+231%). This demand is only going to grow. The global volume of data generated is set to increase tenfold by 2025. The same predictions suggest that sixty percent of this data will be produced and managed by organisations who will need to employ people with the skills for that data to be understood and make it actionable.

Attracting the right data science skills, nurturing that talent and retaining them in a competitive market, is therefore becoming a key challenge for many sectors. This is particularly true of the insurance sector which is seeing fresh data sources emerging to support the understanding of risk and increasing pressure to deliver insights that match the risk of the consumer and at speed. In the car manufacturing market, the data generated by connected cars is both a major opportunity to link with the insurance sector, but also a compliance challenge. 

How are these skills employed at LexisNexis Risk Solutions?

As a leading data analytics and technology provider to the insurance and auto manufacturing markets, LexisNexis Risk Solutions employs data scientists across multiple insurance sectors - from looking at the risk of motor policy cancellation to creating risk scores based on a vehicle’s safety features or precisely mapping flood risk for the home insurance market. There is little doubt that increasingly automated services in the insurance market are demanding increasing numbers of data scientists to both create and maintain the data solutions that enable the delivery of instant quotes to consumers and businesses.

Explain your approach to finding data science talent inside and out of your organisation

To support our large and rapidly growing data science team, we have an ongoing data science recruitment programme which identifies candidates both within the talent pool of graduates coming straight from university and from our existing employee base. This means we have a mix of people either of whom are completely new to insurance and can therefore bring a fresh perspective, or who are very familiar with the sector, engaged with the challenges and revved up to go. Both have their advantages, and neither should be discounted when looking for data science talent.

What makes for a successful graduate programme?

The key, we have found, to a successful graduate programme is to provide as much exposure to the business as possible. This helps create more rounded individuals with regards their experience and investment in the business. Over two years, graduates spend six months in each of our four analytics streams – Telematics; GIS Analytics; Analytics Audit; and Analytics & Statistical Modelling. The programme began recently in the UK and Ireland, but has already been a huge success within our US insurance business.

When we recruit internally, we look for individuals who have a passion for data and a basic understanding of the mathematical concepts behind modelling. It is therefore just as important for the graduates that we expose them to every area of the Analytics and technology business, giving them a broader understanding of the company and their place within it. If an employee later becomes dissatisfied or restless in their current role, they are more likely to seek an alternative position internally if they have a strong understanding of roles across the business. We find this reduces the risk of losing talented people with experience and employees appreciate the lengths we as a company go to in order to retain talented individuals by finding them another position within the organisation. It’s also saves the company a lot of money as the cost of talent acquisition is getting higher.

How do you help data scientists within your organisation expand their horizons?

We are continually working on improving and increasing the opportunities for our data science people to move around between teams internally or internationally. It may sound counter intuitive but offering employees the opportunity to work for our business in another country improves their sense of belonging and thereby job satisfaction. Being flexible means employees feel comfortable to find the role best suited to them, and do not reach a point where boredom or inertia creeps in.

Recruiting is one thing – retaining is another – how do you make your data scientists feel valued?

Most people, in any job, feel more engaged and valued when they are challenged, learning and trusted, the same is true in data science and offering people a sense of ownership is vital. Everyone must feel they are trusted enough to own a certain responsibility, task, project or team. When someone has responsibility for a specific area, they tend to work diligently and be more engaged, improving productivity and morale.

Also, giving continuous feedback without micromanaging is important but can be a hard balance to find. The key is to understand this approach will be different with every individual, and it’s up to the Data Science managers to own that part of the relationship and decide how much day to day management or control or guidance each individual gets.

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.

How crucial is it that the sector makes itself attractive to data scientists?

As we embrace digitisation the demand for skills to deliver value from the growing data pool is increasing. 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.

What would be your advice to someone considering data science as a career?

Honesty! Data Science isn’t as sexy as it seems *cringes after he writes that old cliché*, 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 not all roses and models, it’s a hard fought battle to properly understand data and build something that effectively better predicts an outcome or deploying automation. You have to move from the safety of an R&D environment to a Production environment which is a scary prospect and a hard slog to get all the pieces to align. If you hide the battle scars from incoming talent they’ll start off disillusioned and they’ll become dissatisfied with their role faster. 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.