Starting a career in data science at Sage AI: Conversation between 2 graduates
What is it like to work at Sage AI as graduate data scientists? In this blog, we catch up with 2 members of the team, both recent graduates, to show the people and process behind these innovations.
About the graduates
In October 2022, Bona and Yue joined the Sage AI team as graduate data scientists.
Bona studied for Data Science MSc at City, University of London. Prior to joining Sage, Bona worked as a gemmologist specializing in coloured gemstones and pearls. Whilst studying Data Science, she investigated the application of machine learning to classifying 68 categories of gemstone images, which led to a publication featured as the cover story in a peer-reviewed journal.
Yue studied for Data Science MSc at Durham University. Yue has a background in humanities, having gained a Masters in English literature. During her time as an English teacher in China, she discovered the power of AI-driven language assessments and decided to study Data Science and Machine Learning. She has an interest in NLP and text-generation models.
How have you found it so far?
Bona: It’s been awesome! So far, I have been learning every day and I am developing a clearer picture of the things we do at Sage AI. I very much appreciate the open and diverse environment, where we are given the trust to create a positive impact.
Yue: If I’m being honest, it certainly has been a roller-coaster ride. It has been extremely exciting as I was exposed to new areas every day. I was telling my friends the past few months felt like a whole university degree for me. I get to complete small tasks to help my team, which is really rewarding. But at the same time, it is challenging, as I haven’t worked in this industry before, I need to learn a lot of new things.
What were your first few weeks like?
Yue: It was pretty intense but also fun, because we followed the graduate onboarding programme. Meanwhile, we were getting to know the smaller Sage AI team, i.e. having 1–1 calls with team members to introduce ourselves. Everyone I met was so friendly and always patient to answer my questions. Besides, there was a lot of learning: technical concepts, ways of working, and setting up software on my laptop.
Bona: I agree it was intensive, but it is satisfying to witness my own growth and development over the first few weeks. I started off learning about the current tools and projects at Sage AI, and quickly moved onto building dashboards with Superset, improving existing data science models, and building simple functions and unit tests in Python using poetry and docker.
What were some challenges over the first few weeks?
Bona: I quickly discovered that there is a steep learning curve — our training covers data science and machine learning engineering. While I was familiar with data science, I did not have much knowledge about machine-learning engineering and software testing. To improve my technical skills, I set time aside for learning in addition to completing assigned tasks every day.
Yue: For me one of the challenges was to form a broad understanding of what the team was doing, as the entire ecosystem is very complex. I remember I had to take so many notes every day and of course lots of googling. In the first week, I was unsure about whether I could perform my role well, but with the help of my mentors and line manager, I felt much more confident and less stressed about my work.
How did you end up at Sage AI?
Bona: It was somewhat unexpected that I ended up at Sage AI. I worked for several years as a gemmologist and I always enjoyed dealing with data. During the pandemic, I got some time off and I started researching machine learning. I became very fascinated by it and went on to complete a master’s degree in data science. For my dissertation, I developed a machine-learning algorithm to classify 68 categories of gemstone images which achieved a higher accuracy than myself and 2 other gemmologists. I thoroughly enjoyed the challenge and began exploring opportunities in AI.
While browsing the Bright Network website, I was captivated by the job description for Sage’s Graduate Data Scientist role. I chatted with a friend working at Sage and I found out that the AI team works on innovative data science and machine-learning projects which help accountants automate their tasks. Considering the role aligned well with my interests and recognizing the enormous impact the team has on many businesses, I applied for the role without hesitation. I was really lucky to make it through the selection process and received an offer for the role.
Yue: I’ve heard about Sage as an accounting software company, but I didn’t know much about it. Halfway during my master’s degree in Durham, I was looking for jobs in Newcastle, and I realised the Sage’s headquarters is actually in Newcastle. I was drawn to their belief of bringing your true self to work, so I applied for a graduate role. After my first assessment centre, I was very lucky to be referred by the interviewers to the graduate data scientist programme. So that’s my story.
How was the interview process?
Bona: The interview process consists of multiple stages. Shortly after submitting my application, I was requested to answer some questions about myself in an online form and record several short videos to showcase my skills and knowledge. Then in July 2022, I was invited to attend an assessment centre, virtually, where I was asked to solve a problem with a group of candidates prior to an hour-long individual interview. Shortly afterwards I received an offer.
What were some unexpected things about the role?
Bona: This is my first role in Data Science. Coming out of university where I always worked on data science projects independently, I was surprised to learn that I am expected to read and understand other people’s code while collaborating with the team. Luckily everyone is really friendly here, so it is easy to get help from others.
Yue: Learning how to search the right question is a big one for me, as there could be tons of solutions to a technical problem but not all of them will work for your case. When I’m using SQL, I find out that if I don’t phrase my questions properly in the search engine, I can never get the right answer. So, learning how to phrase the right question is crucial.
Is there anything from your previous experience that helped you in the role?
Bona: With some experience with accounting and procurement prior to joining Sage, I quickly recognize the huge value that Sage AI brings to businesses. In my previous role, I worked in a brand-new team and I was often required to solve problems ranging from identifying unusual gemstones to handling equipment failure. I believe the experience will be helpful when I need to troubleshoot and resolve technical issues. Furthermore, I collaborated with colleagues from the UK, the US, Thailand, Japan, and Hong Kong. Since Sage AI is a diverse team with presence in the UK, the US, Israel, and Australia, I find it helpful to have some experience working with different personalities and across cultures.
Yue: Because I have worked as a teacher, I know there is nothing embarrassing to ask someone for help during learning or just saying “sorry I don’t really get it, can you explain again?”. Teachers love people who raise a question, because it shows they are paying attention. The earlier you let the other person know where your understanding sits, the sooner they can slow down their pace, so you are both on the same page. Whenever my line manager or other colleagues explain a project or technical concept to me, if I’m hesitant about whether I fully understand them, I’ll often try to summarise it in my own words, so the other person can correct me if there is any confusion.
What’s it like working remotely vs. in the office?
Yue: I go to the London office occasionally but mainly working from home in Durham. The London office in the Shard has amazing views and is often full of people, and you can ask questions or have a chat with people easily. But it can be distracting sometimes. I do like the peace and quiet of working from home and having high energy levels throughout the day. But it’s tricky for my brain to separate work from life sometimes as they happen in the same space. It is also hard to ask someone a quick question when you got stuck and just want a second opinion.
Bona: I mainly work in the London office, but I also work remotely from time to time. Same as Yue, I focus better when I work at home on my own. However, working in the office provides more opportunities for social interactions, which are helpful for me to get to know our colleagues better. And I find it easier to seek guidance and insights from other colleagues face-to-face in the office.
What support and resources you have received?
Yue: Bona and I really benefited from having different mentors (Data Science, Machine Learning, and leadership) and we appreciate the emphasis on learning within the team, for example, everyone is encouraged to do some learning during work hours. We also set aside a couple of hours every week to learning programming and other topics we are interested in, using various learning platforms provided, such as Coursera, PluralSight and O’Reilly.
If we were to start our Day 1 again, what advice would you give yourself?
Yue: Don’t be afraid to ask questions. It’s totally normal to feel confused or that things are challenging. Also, go easy on the coffee.
Bona: I received some helpful advice when I started at Sage, and I think the top 3 tips were:
- Build your network.
- Ask a lot of questions.
- Take a structured approach to learning.
If you would like to learn more about the graduate programme at Sage AI, or any other role, please visit our careers website.
