What’s it like to intern at foodpanda?
Find out about our internship life in the foodpanda data team!
Hi everyone! We are three interns from the foodpanda Data team based in Singapore: Jeremy, Angele, and Jerome. In this article we want to share our experiences so far while working in the Data team. We hope this will be an insightful peek into the life of an intern, especially if you are interested in joining foodpanda!
Introducing ourselves!
Angele: Hi! I’m Angele, a Data Analyst Intern with the foodpanda Data team from June to December 2021. I’m going into my third year studying Business Analytics in the National University of Singapore. I have always enjoyed working with numbers and pursuing a degree in analytics has only amplified my passion as I was exposed to the different capabilities of data and technology. Aside from that, I enjoy playing different sports and drawing in my free time!
Jerome: Hello, I’m Jerome! I’m also interning with the Data team from June to December 2021. I study Finance as my major with business analytics as my minor. Generally, I’m interested in reading differing opinions and ideas regarding finance and data science.
Jeremy: Hi there! I’m Jeremy, and just like Angele and Jerome, I’m a Data Analyst Intern based in Singapore. I’m currently a student at Nanyang Technological University pursuing a double degree in Engineering Science and Technology Management. In my free time I enjoy basketball, web-novels, and playing with my calico cat! I started with foodpanda in August 2021 and I’ll be interning until the end of the year.
What we have been working on…
Jerome: Currently, I’m working on a cuisine classification project. This project was for the Content team and it involved using ML to tag vendors to their respective cuisines, which helps vendors to be more easily found by users.
It was challenging initially because of the technical skills required and the business logic that needed to be incorporated. After understanding the problem and constraints well, there were certain custom modelling design choices that had to be made. I found this quite exciting because exploring the unknown from here taught me things that couldn’t be found online, which also made my internship more meaningful.
Also, I had zero knowledge about natural language processing (NLP) at the beginning, which meant that I was able to learn something new every day. I was constantly surprised by how simple solutions could produce very satisfactory results. Data cleaning and error analysis was part of the day-to-day tasks for projects, and I think these are valuable skills to master as it can improve a model’s performance without spending too much time on model tuning.
Angele: One of the first projects that I worked on was to optimise the queries used in the daily and weekly KPI dashboards. These dashboards are used by various teams in the APAC region to monitor progress and provide insights to help the business. As the data sources of these dashboards used custom SQL queries, there was a need to improve the efficiency and reliability of the queries especially when they were being used very frequently.
Personally, this was an incredibly significant project for me as I gained a much deeper understanding of the data structure in foodpanda through a hands-on process. It taught me about the many small, yet important details in the Data team’s technical workflow that allowed the team to work more efficiently.
Another project I had was creating a sanity check dashboard. While the KPI dashboard was for business metrics, this dashboard was to aid analysts in flagging any data-related issues in specific tables or metrics. I enjoyed working on this as I had a lot of autonomy in deciding the details and design of the dashboard. Even though I had used Tableau before in school projects, I still managed to learn a lot as I had the opportunity to apply lesser-known functions like colour schemes and user filters.
Jeremy: Recently, I’ve been working on predicting customer spending behaviour using machine learning models. The output of the model helps us identify trends in customer behaviour and allows us to create a more personalised experience for foodpanda users.
I’ve been mainly working with ML Engineers and Data Analysts to test out improvements and analyse results, and I’ve also created dashboards for stakeholders to track the model’s performance and visualise the prediction results. It’s been quite fun to learn hands-on about the pipeline in a machine learning project that involves huge datasets, and also about the various ways we can analyse the results to drive action.
My undergraduate specialisation is in mechanical engineering, so I haven’t had much experience with the software that’s used in tech projects. So it was really exciting for me to work with things like Git, Apache Airflow, and Google BigQuery. It’s also been pretty cool to get a glimpse behind the scenes of how foodpanda handles huge amounts of data (big props to the Data Engineers), and also how the Data team works towards growing a culture of data literacy by creating educational resources that are open to all employees.
Some challenges that we faced at work…
Angele: One of the initial challenges I faced was on the local environment setup as it was something I didn’t have any prior experience with. Hence, troubleshooting the setup of Docker, Airflow, etc. was tough with virtual communication. However, there were many guides provided (such as Dataversity) which were very useful, and with the help of the team I managed to set up everything and can now work smoothly.
Jeremy: For me the initial stage was rather challenging as there was a lot to read. Setting up my local environment was one thing, then I had to understand the structure of the codebase and go through the documentation on existing projects. Thankfully, my managers were very approachable and helpful which made things a lot smoother especially when working remotely. It was honestly quite rewarding to slowly feel yourself figuring things out after the first week!
Jerome: My major is in Finance, so I’m not from a particularly technical background, which was why I initially struggled with writing and reading code and using Git. However, the well-written onboarding documents and help from my colleagues definitely made the learning process much easier for me! Also, I think the constant exposure to the problems also helped because it gave me the opportunity to improve quickly.
How interning during COVID was like, and some of our personal takeaways…
Jeremy: This internship so far has been really rewarding for me. Actually, I quite enjoy the freedom that a remote working arrangement gives you. I’ve had more time to learn and complete my tasks, especially with the time savings from not having to do the daily commute back and forth. Though I must say I’m looking forward to visiting the new office when it’s ready!
At least for the Data team, I think that the pandemic hasn’t had that big of an impact on the stuff that we can do. It’s also quite cool that I get to work with a diverse group of analysts, ML engineers, and data engineers from a variety of nationalities and career backgrounds. Recently, I was even able to join a sharing session where the Berlin Data team exchanged ideas with us on how to better model customer spending.
Apart from that, a key lesson I learned was to not hesitate to ask questions! I think interns (or anyone for that matter) shouldn’t worry about looking dumb. Although for me, the rule I set for myself was that I ask only after I’ve tried at least twice to figure things out on my own.
While working on my tasks, I realised along the way that I could solve some issues just by staying with the problem for slightly longer. Even if I still ended up stuck, digging deeper allowed me to reframe the issue and pose a better question than the one I initially had. In a nutshell, this helps me ask better questions!
Jerome: The experience so far has been great! To me, colleagues are actually more approachable since everyone is just a call or message away. For me, my takeaway is that having good communication is important. I think by letting people know what you are working on, and also what you are about to do, it creates the opportunity where useful tips can be shared to reduce the learning curve for everyone involved.
Angele: Interning in foodpanda has been very fulfilling and exciting! I’ve managed to work on different projects, learn various technical skills and even soft skills. Though, I think working remotely was definitely a concern for me when I started. I was worried about the lack of opportunity to interact and seek help.
However, I have to say that I am incredibly thankful for my managers and also the team for being so supportive in providing the help I needed to get on board. I think we can all agree that the pandemic has taken a toll on everyone, and everyone had to adjust in their own way. Knowing that, I simply hoped to make the most of my internship here and to learn as much as I could from the entire team. In fact, I chose to take a Leave of Absence from my studies to take on this internship and it has been a rewarding experience so far!
Skills from school that we think are useful in our roles…
Jerome: To me, being able to understand and implement research from others is a key skill. The problems faced as a Data Analyst are diverse and therefore reading and digesting research by others helps you go a long way. After all, teaching a man to fish feeds him for a lifetime.
Angele: Studying business analytics provided the basics of many technical skills that were useful in my role as a Data Analyst intern. It provided me with prior experience in Python, SQL, Tableau, and Git. However, what I learnt in school wasn’t always enough, as many courses only briefly introduced a software or programming language. Additional courses outside of university also helped to provide me with more in-depth knowledge that came in useful during this internship.
Jeremy: I think the ability to communicate your work is super important when working in a tech-related team. Many modules in school have a presentation or report writing aspect that many often find a chore, but I think it’s something that’s hard to get right if you don’t consciously work on improving it every time you do it. Especially since you don’t really have that many chances to practice it “for real” before you take on a full-time role. The communication skills I’ve picked up have helped me to better express myself even in things like explaining a problem I’m facing while asking for help.
Advice for students who are thinking of doing an internship…
Angele: Taking on an internship in the field of your interests can be very beneficial and fulfilling. I’m super thankful for this opportunity as it gave me a closer look into the life of a data analyst that I feel would’ve only been possible through an internship.
I find it important to try out the things you might be interested in, especially as it may not always be what you perceive it to be. This applied to me even when choosing a course to pursue in university. With every opportunity, it’s always important to start with an open heart and mind, to try your best in order to make the most of it. That is a mentality I hope to continue with as I pursue my career as well. All the best!
Jeremy: My advice would be to not be afraid to apply for a role even if you think you are underprepared. In my case, I wasn’t from an analytics program, so I didn’t have any real reference point on how much I should know to qualify for a Data Analyst internship. I just learnt stuff on my own and gave it a shot!
So for anyone who’s not from a tech background, I’d suggest you learn as much as you can on your own or in school and then apply for the role, as the process itself can also be a learning experience! For me, I picked up SQL, Python, and Machine Learning on Coursera and applied to many data-related internships which gave me many opportunities to improve my skills in the various interviews. Most interviews involved SQL tests where you’d have to apply joins and window functions to retrieve the required data — so focus on those skills!
Jerome: I think that apart from the basic technical skills for a role, learning how to communicate effectively is just as important as well. You will be dealing with various stakeholders that require clear explanations in order to trust your work enough for deployment.
My advice for applicants would be to focus on demonstrating your reasoning skills in your interviews, which means clearly explaining the what, how, and why of your past projects. This is because data roles require one to really understand the business problem in order to develop a targeted solution. So if you worked on an ML project, being able to communicate why your models are designed in a certain way helps to show interviewers that you know what you are doing. Good luck!
Our favourite things to order on foodpanda!
Jeremy: I think it’s fun to order different snacks from pandamart to try! Recently I bought some tortilla chips and a jar of Tostitos cheese to go with it. It’s super tasty especially if you heat up the dip, and also it’s nicer than those that you get at cinemas.
Jerome: Personally, I try not to spend too much time on deciding what to eat, so I usually just default to Pastamania!
Angele: I really enjoy ordering food for my friends and family, as when I enter different addresses, I get to see the wide range of food options available all around Singapore. For myself, I usually stick to a few safe options that I know I’ll enjoy such as Stuff’d. A great option is also to order pick up in advance if you’re heading out. It helps to save time and money with the discounts!
Interested in a career in data? An internship is a great way to learn more about specific roles as well as the culture of the company. Check out the roles available at foodpanda here!
