Hello again, everyone!
Last weekend, I was in Munich and got to try some amazing German and Bavarian delicacies. I had ox sausages, German potato salad, schnitzel, freshly baked Bavarian pretzels, and some incredible ice cream from the oldest ice cream parlor in Munich. Apart from the food, I also got to explore the city during the onset of summer, which was an incredibly lively time. The parks, streets, and public squares were full of people enjoying the sunshine, and the whole city had a fantastic energy to it.
In terms of work, it has been a busy but rewarding week. I finally wrapped up my first two internship projects and received some great feedback from my managers. It felt amazing to see the analyses come together into something genuinely useful for the team and something I can look back on with pride.
What really surprised me while reflecting this week is that it has only been four weeks. It feels much longer because I have learned so much from the people around me and from the projects I have worked on.
The learning has ranged from soft skills to technical skills and everything in between. On the technical side, I have spent a lot of time programming, making API calls, learning how to modify endpoints, build payloads, harmonize data, and generally become more comfortable working with real-world datasets and systems.
However, I think the most valuable lessons have been what I like to call the “in-between skills.” One of the best pieces of advice my manager gave me was to always ask why. Whenever I am given a task, she encourages me to first ask: Why are we doing this? What is the purpose? How will it be used? In a broad field like data science, analysis can go in a hundred different directions. Understanding the purpose behind the work helps narrow the focus and ensures that the final product is as useful as possible for the team.
Another important lesson has been learning how to tell a story with data. Rather than simply presenting numbers or results, I have learned the importance of framing questions and building a narrative that guides the audience through the analysis. Each question should connect to the next, creating an interlocked story that ultimately answers the problem you set out to solve. This is not advice I had heard much before, but I can already see how valuable it will be throughout my career.
The third lesson has been understanding the domain behind the data. It sounds obvious, but in a field as specialized as soil science, it is easy to run an analysis, generate results, and move on. What I have learned from my team is the importance of digging deeper. They constantly encourage me to ask subject matter experts what variables mean, how different factors interact, and why certain relationships exist. That extra layer of understanding is what transforms a basic analysis into something truly meaningful and useful.
On the soft skills side, I have learned a lot about connecting with people and asking for help. Because I am working in a smaller startup environment, it has been easy to meet people from a wide range of departments and backgrounds. More importantly, I have had opportunities to work with them. Whether I am asking the modelling team questions about model outputs, talking with the engineering team about data interfaces, or discussing datasets with the data science team, I get to interact with people across the company and learn from their expertise. Not only has this helped me improve in my own field, but it has also given me insight into many others, which has been an amazing experience.
Looking back on these last four weeks, I can clearly see how much I have grown and learned, both professionally and personally. It has been exciting to reflect on everything I have experienced so far, and I am looking forward to continuing that growth in the weeks ahead.
Until next time, folks!
