N-Things I Learned At My Data Science Internship
Key takeaways from my experience practicing data science in industry.
For the past 3 months, I’ve worked as a Data Science Intern at MailChimp in Atlanta, GA. Prior to this, all of my work has been from courses, independent projects, and conducting research at Quinn Group. Through this experience, I’ve picked up a few things that contributed to my personal and professional growth. I’d like to share some of the things that I learned this summer!
It’s a lot more than just data science
Fortunately, none of my work as an intern involved getting coffee for full-timers. Instead, I was integrated into my team’s ongoing projects as if I were a regular employee. Given this responsibility, I was able to do a lot of things outside of the typical data science pipeline.
I met with cross functional teams, constructed dashboards, explored edge cases, ran models, presented my findings, conducted research, coordinated with my product manager, characterized data, put together reports, performed statistical analysis, created quality assurance test, wrote production code, and engaged in pragmatic planning.
All of which gave me valuable experience way beyond what I had originally anticipated. By the end of the summer, I felt so involved in the inner workings of my projects because I had done so many things. Through this process, I became comfortable with wearing different hats.
Getting settled into your work environment
Your first week should contain no real work. You’ll have plenty of time to do this in a standard 12-week internship. So, don’t try to bang out a million lines of code to impress your manager.
Instead, you should focus on settling in, learning about the projects you’ll be working on, asking important questions, learning how to navigate your office, setting up your virtual environment, and getting to know your coworkers!
This ensures that you’ll fully understand your team’s trajectory, complications, and how you can make an impact. It also gives you the chance to properly introduce yourself to all of the people that you’ll be interacting with on a regular basis.
Embracing feedback
Learning how to give and receive honest feedback was one of most beneficial things that I learned this summer. My data science team embraces the feedback approach covered in the book Radical Candor by Kim Scott. In summary, frequent and honest feedback should be:
- Actionable. Because no one benefits from comments like “Your notebook sucks,” or “That’s a terrible plot.”
- Pragmatic and respectful by mastering the art of thoughtful disagreement.
- Established as normal by making it a habit in the office.
Most importantly, all constructive feedback should be well received. You should let your commenter see your enthusiasm and accepting attitude, so they’re more comfortable voicing their honest opinions. That way you only get the most helpful feedback.
Communicating outside of your team
You’ll need to be comfortable presenting your work to people with no knowledge of data science concepts. This can be challenging because you’re probably used to communicating these ideas to people on your team. The trouble is that people outside of your team won’t have the same backgrounds. To make matters worse, lots of experience can make you a bad teacher, since you no longer have the perspective of someone that is new.
“If you can’t explain it to a six year old, you don’t understand it yourself.” — Albert Einstein
A few guidelines that I learned were make sure that your objective is clear, use real world examples, limit your use of complex math, and know your audience.
Finally, you’ll want to get feedback from someone with fresh eyes. This is a person who doesn’t know too much about the details of your work. Talk to them about your work and present it how you would normally. Read their expressions and see how well they follow along.
If they understand it completely without a hitch, you nailed it! If it goes completely over their head, then you might want to simplify your terminology and visuals.
Be a representative for your team
At many companies, data science doesn’t have the extensive reputation that other departments like software engineering might have. In this case, your role and projects will be less understood by your coworkers. So, it’s especially important that your team’s intentions, progress, and expectations aren’t taken out of context.
You can do this by clarifying things with your managers and team. If you are asked about something that you are totally uncertain about, it’s okay to kindly admit “I don’t know.”
The last thing that you want to do is speak on something that you aren’t sure about and unintentionally misinform or overcommit to something on your team’s behalf.
Staying inside of your scope
When assigned to a ticket, the business goal, technical goal, and definition of complete should be well defined. If they aren’t, work with your team on refining the task. This will help you with managing expectations and reducing the chances of scope creep.
Scope creep happens when you continue to add subtasks to your ticket after you have started working on it. Each additional subtask expands the scope of your ticket and once you start incorporating all of these new ideas, your once bitesize ticket grows to the size of several tickets. Then before you know it, you’re losing sight of your ticket’s goal by working on something way outside of its original scope.
From my experience, tickets involving exploratory analysis were most susceptible to scope creep. I kept following one idea to the next, and before I knew it, I found myself in a rabbit hole.
It helped me to only focus on the minimum requirements defined by my ticket and take note of any additional tasks that I came up with. Once all of the minimum requirements were fulfilled, I could go back over it and decide whether any of those subtasks provided any new information to my analysis.
Often times, I came to the conclusion that these subtasks were redundant and that my working notebook already conveyed everything it needed.
The internship is for YOU
Your internship is meant for you to bridge the gap between school and industry. Take advantage of this unique learning opportunity. You should be developing your strengths, learning new skills, networking, and doing meaningful work.
The best part is that after your work day, you can relax and destress without that guilty conscience you have while you’re in school. Clock out and don’t feel the need to do anything related to work. You’re simply free to enjoy the rest of your day!
