David Reed (DR): You’re a data scientist working in in the data department at Zurich Insurance, but that isn’t where you started out. Could you please tell us a little bit about your initial career?
Hannah Lee (HL): “When I finished my A-levels, I wasn’t sure if I wanted to go to university or not, so I started looking for higher-level apprenticeships. At the time, they were not a big thing, nor were there many around. I went looking for apprenticeships that had a maths aspect to them as it had been a constant for me, even from an early age.
“I found an actuarial apprenticeship at Zurich and that’s how I entered the world of insurance. My first role was in a business insights team in pricing focused on reporting, dashboarding, and optimising the portfolio. Although it was quite traditional, I think it was the perfect start for my career because it allowed me to learn loads about insurance and work on different parts of the business.
“I was still very new to the company and had no idea what was next. I had started to teach myself programming and the manager of the data science team gave me the opportunity to join her team. I wasn’t sure if it would be too advanced for my skills at the time, but I went for it anyway.
“I learned so much in the first six months even though I didn’t really know what data science was. I never expected that it would become my career.”
DR: Did you have any insight into what data science was doing within Zurich? Had you seen any presentations about the work?
HL: “Some elements. It was sold to me as this crazy advanced stuff and that most of the people in the team had PhDs – I was just straight out of my A-Levels. Honestly, it was nerve-wracking. I thought, ‘there’s no way I’m going to be able to do this’. I initially worried I would be doing menial tasks.
“But what actually happened was that the team threw me in the deep end and my manager got me working on some quite technical projects, with support of course. I learned so much very quickly.
“That was the beginning of my data science career, and I am currently in my final year of a data science degree apprenticeship. I study one day a week at university and the rest of my time is learning via work. It’s a balancing act between self-study and work.”
DR: Many of our member organisations offer apprenticeships. Could you provide a sense of what it is like to be a student on one of these apprenticeships?
HL: “When I am at work, there is no label hanging out there stating, ‘I’m the apprentice’ – I’m just part of the team. In fact, there are multiple members of our team doing the same apprenticeship at different stages. There’s no difference to anyone who is or isn’t on the apprenticeship.
“A big thing that I’ve learned is the need to be flexible. For example, when it comes down to deadlines, exams and assignments, I may need a little bit of extra time at work to study and prepare. However, when I’m then out of university on the summer break or similar, I can make the time back and focus on the job.
“There is a balance and strong sense of trust between me and my manager of my time on- and off-the-job for the apprenticeship. It can be hard work and I think some people think an apprenticeship might be an easier option to going to university full-time. But it’s a challenge when you’re doing the same qualifications as people studying full-time but completing it in just one day a week.”
DR: Does the support from your management and the team give you any insight into up-skilling people in data? How could organisations make that work for other colleagues who perhaps don’t come from a maths background?
HL: “I am a big advocate for apprenticeships and think these should be open to people of all ages and stages of their careers. There are so many data-focused apprenticeships that allow individuals to learn new skills and provide opportunities to adopt data into their roles or even change career paths.
“Data literacy is so important across organisations. I think people need to understand and be comfortable with the potential of data and its importance in making data-driven decisions. It’s not this big, scary concept of robots taking over the world or anything like that!
“There also needs to be an element of understanding that everyone in the business has an impact on the quality of data and the quality of the insights drawn from data. My team has carried out data quality training across the business which involved educating underwriters on the importance of inputting data correctly and how any system inputs will come through in the data.
“However, it’s easy to put someone on a course and say, ‘now you know data!’ Support from your manager and team is needed to identify that you can actually apply those newfound skills in your role. It needs to be seen that you haven’t just received a certificate and ticked a box.”
DR: We speak to many data leaders about career paths with some stating they have long-term, multi-year plans. As someone that never really considered working as a data scientist, do you have a plan? Do you see a career stretching out ahead of you?
HL: “I actually had this conversation earlier. I feel like I am at a crossroads where I’m not sure which direction to take in the world of data. I could go down the technical path or take a step back and explore the business and strategic side.
“Even in the last couple of years since I joined the data science team, there are roles available that weren’t when I started, which makes it difficult to see where I could be in two years’ time – the role may not exist yet! We have evolved a lot as a team – there is a lot more focus on production and implementing data science solutions, which has introduced new roles. There’s also the data science consultancy section, focused on integrating data science into the business and scoping new ideas.
“Leadership does appeal to me, but, again, it’s figuring out how to get there. I want to progress in my career, and I have ambitions to do so, but I need to figure out how it can happen, what skills I need and which direction I should take. There are schemes around that can catapult people moving into leadership and there are mentoring programmes.”
DR: To people considering whether to go to university or not, would you recommend the apprenticeship route instead?
HL: “Absolutely. When I first started my apprenticeship, I wasn’t sure if it was the right decision. I had friends going to university and living these amazing lives and I thought that because I was labelled as an apprentice at the time, I was going to be the apprentice forever. But now I think my experience is much more valuable than what I would have learned at university.”
DR: It sounds like you gained a lot of experience moving around the business. Should apprenticeships be built with the aim to move people around various departments?
HL: “Yes. I’ve gained a lot of value from having that business background and getting a good understanding of what each business area does. Rotating around teams is also valuable for building relationships and networks.
Some of our graduate schemes now rotate through data engineering and visualisation to provide a background of the different data areas, which is useful for a well-rounded experience.”
DR: As a young female, what do you think are the key drivers to encourage people to apply for these kinds of roles? Are there many female role models in the organisation and is that important to you?
HL: “I think when it comes to applying for jobs it’s almost too late. Young people need those influences earlier on. A lot of outreach programmes, such as Girls in Data, go into schools and hopefully inspire the next generation. Young people need to be educated about data roles they have never heard of or would have never thought about before they start picking what they do at college or university.
“In the same vein, businesses using social media campaigns for recruitment can access a wide range of potential talent. However, it is important for young people to see someone that they can relate to when going into a career, whether that is someone of their ethnicity or gender. Some stereotypes about data professionals are also untrue. In reality, there is a diverse range of individuals in data roles with differing backgrounds, interests and personalities.
“For me, someone like Anita Fernqvist – Zurich’s COO – being in the position she is in is inspiring because it shows that someone from a data background can reach great heights. But in general, there needs to be more female role models in data.”
To get involved with the DataIQ Awards for 2023, click here and register your interest to enter.
To nominate yourself or a colleague as a data leader for inclusion in the 2023 DataIQ 100 UK for free, click here.
To nominate yourself or a colleague as a data leader for inclusion in the 2023 DataIQ 100 USA for free, click here.
