Meet Lauren, Data Science Degree Apprentice
I am Lauren Gilbert, a Data Science Degree Apprentice at Haleon, in London, England.
A bit about me
Whilst at school, I really enjoyed subjects that had elements of problem solving and understanding human cognition or historical events. These interests helped me pick my A-level subjects: Math’s , Psychology and Chemistry.
After finishing my A-Levels, I went to university and studied Psychology for a year, a subject that fascinated me but didn’t sit right in terms of career aspirations. Math’s was my favourite subject throughout school, but I couldn’t see myself as a “mathematician”. At the time, I didn’t know of the multi-disciplinary data science field, that would enable me to continue learning and (more excitingly) using mathematics as well as other subject domains like computer science.
Why did you choose this career path?
Knowing that my academic and professional interests required a change from the traditional university route, I took the opportunity during the pandemic to look into alternatives with one being apprenticeships, where there is a blend of multi-disciplinary learning and real-life application.
I began a level 4 Data Analyst apprenticeship with Unilever, an international Fast Moving Consumer Good’s company, in a Data Product team. Here I was introduced to all things data, most importantly: data science.
From my growing data science enthusiasm, combined with a passion for healthcare with a human-centric approach, and a love of the apprenticeship route, I applied for a degree apprenticeship in Data Science with Haleon (formerly GSK Consumer Healthcare), that partners with Anglia Ruskin University. It was clear from the interview process that there was a strong data science team that made use of new technology, which would mean my apprenticeship would be challenging and relevant in the ever-growing space of data science. Working in a strong data science team is particularly important because a degree apprenticeship has two qualifications, the degree and an apprenticeship qualification which proves I can apply the content of the degree to business-critical projects.
What is your data science specialty?
In my previous apprenticeship, my focus was on data visualisation using Power BI and ensuring the end users (over 1,000 colleagues across various countries and sectors) were happy with their reports. My transition into Haleon and Data Science meant taking a step back from the front-end and end-users in some regards, focusing more on back-end changes and improvements to a dashboard in the development stages. However, from my previous experience, I have learnt to always keep the end users in mind because there is no point building or adapting a model or piece of work if it won’t fulfil the needs of the end user.
What do you do day to day?
My daily routine varies, but I typically split my day into two distinct focuses to better manage my energy and productivity. In the morning, I may concentrate on enhancing a dashboard that monitors the performance of our data science models, then in the afternoon, I might work on a university assignment or improving code quality. This way, I can maintain my momentum throughout the day and achieve progress on multiple fronts.
I have now rotated into my next team, which is into the Data Innovation team. I attend daily scrum calls and will begin to use Jira to manage my workload.
It is also important that I maintain an 80:20 split between work and study towards my degree and apprenticeship qualification. There are virtual university sessions for about 5 hours a week, as well as coursework and assignments, plus the occasional week in university. This is a great time to meet apprentices in other companies.
Are there any areas of Data Science (DS) / Machine Learning (ML) that you feel will be particularly prominent in the future?
Yes, but to pick just one area is difficult. Data is growing exponentially, and technology is becoming more equipped to store, clean and compute this data and produce actionable insights. I feel that DS/ML will be used in creative ways, for example to improve the ways of working across a breadth of sectors and worker skill levels. Tools such as ChatGPT, that use NLP, are being used to help with coding and writing business documents.
A variety of job tasks can be automated and assisted with AI/ML, which can help shorten the working week (particularly interesting with the 4-day week being a hot topic!) For example, Ocado uses automated warehouses that have 3,000 robots and an AI Air Traffic controller, to optimise the supply chain. Other areas of Data Science and Machine Learning can be used such as NLP, in creating dashboards that have “smart insights” enabled, so users can ask natural language questions relating to the data, thus reducing the need for team members to create their own reports and analysis for every query. Through using Data Science and Machine Learning models, we can make sure we use our time on the most important tasks that require people (not machine), such as those requiring creativity and understanding of the clients.
What’s your top tip for anyone who wants to enter the field?
My top tip would be to understand the reason for why data science is important and understand your own values and interests to help you know where you want data science to be applied. Acquiring technical skills, such as Python to build models and GitHub for version control, are fun to learn and important to be proficient in, but alone they are not sufficient if you are not working on projects or in an industry that you find meaningful.
What do you like most about working for Haleon?
Despite being a relatively new team, Haleon’s Data Science team boasts a wealth of expertise in NLP, Computer Vision, Mathematics and Statistics, to name a few. I love working with individuals in Haleon who are not only proficient in their respective fields but are also genuinely passionate about data science and Haleon’s purpose to deliver better everyday health with humanity. Furthermore, the team’s willingness to help and support one another make for an enriching work environment. For instance, with the use of a buddy scheme, where apprentices who have been with the team for over a year have regular connects with new apprentices to offer support.
Recently, Haleon sponsored a Women in Data event which I attended. It was an inspiring day with various formidable women who have faced challenges in their professional lives because of their gender. It was amazing to hear from Jill Scott, one of the Lionesses, who discussed the data behind the England women’s football team, data that helped them win the EURO 2022! From colleagues to opportunities available at Haleon, it is clear Haleon is inclusive and care about everyone succeeding.
