We’re proud to be a company that encourages colleagues to be themselves. We believe diversity of thinking and different perspectives strengthen the way we work and makes us successful. We are proud to be partnered with Women in Data UK (WID UK) for a second year. 

Meet some of our specialists who share with us why data is important to them.

Hebe.jpgHebe Gilham – Pricing Analyst

After studying Maths at university, I was delighted to find a role where I am able to apply the skills I learnt. It is fascinating to uncover customer trends in large datasets and to impact findings with price changes.

My regular tasks include coding to generate reports on the vast amounts of data we collect, and identifying areas where we can optimise our pricing. Within Hastings I have had opportunities to collaborate with other teams - assisting with analysis and marketing projects, as well as being involved in multiple different areas within Pricing. I have learnt many new skills including SQL, R, statistics and soft skills. These paired with a positive work environment means that Hastings has been a great place to kickstart my career.

Lorraine Allchin, Senior Data Scientistwomen in data- Lorraine Allchin.jpg

I have always had a passion for mathematics. It uncovers how everything works and is the perfect lens to understand, well… life, the universe and everything. My enthusiasm for learning drove me to complete a DPhil (Doctor in Philosophy) in Genomic Medicine and Statistics at the University of Oxford, which had been a childhood dream.  When I decided to leave the academic world, I spent two years at RiskFirst developing analytics for Direct Benefit Pension Schemes before moving to Hastings Direct in 2015.
At Hastings, I began in Retail Pricing, where I worked to develop innovative new pricing methodologies for both Home and Car as a predictive modelling expert. After a few years I moved to the Underwriting Services function of the business and as a Senior Data Scientist, now focus on risk quantification utilising cutting edge machine learning and supporting best claims practice. When I first joined Hastings I was the only female member of the Pricing team. Being a champion of inclusion and knowing first-hand the importance of diversity in data especially, I have worked to help improve the diversity of the analytical teams in the business, be a mentor and role-model to other women in the fields and have been part of our gender diversity forum.

It can be disheartening when we advertise a new role and receive CVs from a male dominated pool of applicants.  If girls continue to look at top businesses and see tech roles as something that men do, many of them will continue to miss out on opportunities to work in this rewarding field.  We all know that algorithms reflect the biases of their designers, through the data on which they are trained.  Improved diversity within the field allows us to challenge these biases and work to deliver solutions that benefit our customers and our companies.  This is why events such as Women in Data are so vitally important to the future of data analytics and I am so pleased that Hastings Direct is once again sponsoring this initiative.


Helen Edwards LR.jpgHelen Edwards, Actuarial Modelling Manager 

I am the manager of the Actuarial Modelling team at Hastings Direct and have nine years of experience working primarily in pricing within the general insurance industry. I attained a first-class honours degree in Mathematics whilst working full time and I am currently studying the actuarial exams as a member of the IFoA (Institute and Faculty of Actuaries).

I have always loved learning new things and discovered my passion for statistics at College. Data has been a big part of my working life since I first started as an MI Analyst producing reports for management. I am now managing a team whose primary focus is building statistical models and I enjoy finding the unexpected within our data. 
I live by the beautiful sunny seaside in Eastbourne.

To find out more about our Data Science teams and opportunities click here.