An Internship During COVID: My Experience at VIDA & Co
In truth, this internship opportunity came to me a bit unexpectedly. A friend of mine contacted me to tell me that his roommate was looking for a Data Analyst intern for their company, VIDA. It was already September, so the Fall semester had already started, and I’d finished finalizing the classes I was taking. Even with an all-online semester, it was clear that I was going to be busy with classwork, so I had suspended looking for an internship or post-grad job until the following semester (Spring 2021). I was quite surprised at first by the offer, as I had not been actively courting opportunities, but based on my interest in the work VIDA was doing, I quickly responded that I was interested. I knew finding these types of chances in the current market climate would be tough, so I felt motivated to give it a shot. I sent VIDA’s team my resume, and just two days later I was talking to their team on an introductory call. That weekend I had a technical interview, and the following Monday I had an interview with my would-be boss. In the end, I was offered the internship position within a week of first applying.
I think there are two reasons why I got the internship so quickly and (relatively) easily. First, since I entered the Business Analytics program last year, I’ve been putting effort into networking through various meet-up events. I have been going to a variety of events, not merely those related to “tech”, and found myself able to meet engineers and data scientists at practically every one (a clear advantage I now see of being based in the SF Bay Area). Perhaps not coincidentally, the friend who introduced me to this internship was someone I met at a completely non-tech related meetup.
Second, I have 10.5 years of experience working as a software engineer in Japan (at a department store), so I bring a lot of knowledge to the table, specifically about data and systems in retail, such as in the fashion industry. Given VIDA is a start-up selling custom-made fashion items, it was a perfect fit! In all, their core business, coupled with my experience and desire to be involved in retail/e-commerce after graduation, both came together to help make this internship happen, and augment my ability to learn during the course of it.
Working at an e-commerce company during Covid-19
The company I joined, as I mentioned earlier, is a fashion e-commerce start-up for custom-made items. Artists from around the world upload their designs, and consumers can choose from a combination of those designs and products (such as scarves and accessories). The ordered items are manufactured and shipped from factories in India, Bangladesh, and elsewhere, using proprietary printing technology, allowing them to operate without wasting inventory. However, this year’s Coronavirus situation has led to fewer people being able to leave their homes, more people losing their incomes, and consequently fewer people buying luxury fashion items. While people are shifting to shopping online, there are many e-commerce companies that have been forced to respond & pivot in order to survive.
What my company did in this situation was very simple. They launched masks. The launch was just a few days before the CDC changed its policy to suggest, and/or require, wearing masks. I joined the company much later than this mask launch, and when I looked at their internal data, it looked like this pivot had a massive positive effect. For example, sales in April, when the company started selling masks, were 50 times higher than in March, and from May onward, sales were still 20 times higher than before the masks were sold. While the company was able to significantly increase sales as a nimble start-up able to pivot strategy quickly, this increased demand did bring about a few unanticipated issues. While an arguably good problem to have, from this April launch up until I joined in the summer, they had greatly exceeded the capacity of shipping and customer support in their warehouse locations. Not only this, but they were beginning to discover limitations to their data management systems and methodologies, namely relying on spreadsheets that had become messy and unruly due to the quantity of new data. Around the time I joined in September, they had begun the process of analyzing this messy data that had accumulated up to that point, and I was invited to help in this process.
Analytics and challenges in the e-commerce start-up
The actual work I was involved in was in response to a request from the CEO and operations team to aggregate the number of customers since the mask launch, the repeat rate, and how long it took for them to place a second and subsequent order. There were several challenges that emerged here, but the biggest problem was that the necessary data was scattered across multiple locations and spreadsheets. As mentioned earlier, prior to the mask launch, they had been entering data downloaded directly from Shopify into a single spreadsheet to keep track of sales and costs, which had been sufficient up until March of 2020.
To manage the sudden increase in data, the engineering team built a data pipeline from Shopify in July, and the data was being fed into BigQuery, but even when I joined in September, the content was still not validated, nor being used effectively as it could be. Once the output was captured, it was sometimes necessary to double-check it against the data downloaded from Shopify, and the data in the master spreadsheet. Furthermore, while the (1) Shopify data is managed by SKU (every time an artist uploads a design, a SKU is created and linked to Shopify), the (2) information required by the operation and the lead teams is on a per-product style basis, so we needed to fill in the gap between those 2 areas to create the final output tables (of the data). Since the master data is just a spreadsheet for the operations team to work with, the process of using Python to combine the data from BigQuery and the spreadsheet data into the required output was eventually settled. Right now, the engineering team is starting a project to clean up the BigQuery data and make sure that all the work can be done without the need for spreadsheets and downloads. The python scripts I wrote will also be incorporated into the automation process after the data platform is complete, so that the operations and marketing teams can retrieve key data themselves.
Entering a new environment remotely
Finally, I would like to share my thoughts on remote work in the current, particular environment. Although I was familiar with the retail industry, I still had some initial apprehensions about a remote-internship. This was also my first job as a Data Analyst, and notably, my first job in the US. My teammates and my immediate supervisor took that situation into account, and communicated with me frequently and with care, which I greatly appreciated.
As for my initial apprehension, part of that stems from my doubt about the effectiveness and ability to ‘team build’ and be integrated effectively in a fully remote environment, such as the one I stepped into.
Coincidentally, I was able to run this experiment on myself, with the result being that I found it to be somewhat difficult. For one, I felt that the most important thing was to consciously communicate with others in this environment, and whether such a communication platform could be successfully rolled-out to connect all remote workers. While I tend to be more introverted, in-person environments tend to help bring me more out-of-my-shell, and I find it much easier to communicate effectively with others. A quick chat in the office hallway or a relaxed lunchtime doesn’t exist online. However, such communication is an important element in building a sense of unity as a team. Existing members have the time and memories of working in the same office, but new members don’t have that. As we enter a new environment online, we will have to communicate more openly and proactively than ever before, and as a team, we will need to provide those places and opportunities for the new members. Of course, the company will revolve around the work that’s there, and some people may not see the value in non-work-related communication. But when work is an activity that takes up half of people’s day, I like to look for human connections and fun moments in all of it.
To sum up, I have now been working as a data analyst intern for 3 months. In all, I have realized that it is essential to have an environment that allows me to take the various methods I’ve learned in the Business Analytics program and apply them in the field. The systematic infrastructure, the organization of the data, and the executives’ understanding of it are the minimum requirements for effective use of the data. Of course, it is possible to create some kind of model, whether it is a spreadsheet or a database, as long as you have the data. But the most important thing is to use them to make decisions, to connect them to profits. Gaining this perspective has been a great learning experience for me in my future work as a data analyst and scientist.
