5 Tips for Transitioning from Finance to a Career in Data
By this time of year, thousands of recent grads have begun a career in Finance. For many young professionals, this career path will stick — and that’s great! A career in Finance brings a lot of early exposure to different companies, industries, and management styles. If you’re like me though, it might be apparent fairly quickly that Finance isn’t quite the right fit. The good news is, that’s also ok!
If you enjoy the modeling and analysis side of the job, transitioning to a career in Data might be a great choice. Below are some tips to get you started:
1. Learn some SQL
I know, I know — you barely have free time as it is, when the hell are you going to learn an entirely new skill? Trust me, I get that… but it is doable. Think about all the times you see your colleagues subtly prepping for interviews, going to “doctors appointments”, or just killing time between turns on a deck. Using those little moments of respite for more work might not sound exciting, but I promise, it can be well worth it!
When I was in Investment Banking, I had literally 0 exposure to or knowledge of SQL. It was only upon interviewing for an analytics job at a FinTech firm (yes, I should have prepped more… it was not my best interview) that I learned there was an entire world of modeling outside of Microsoft Excel.
Following this realization, I took the advice of my interviewer and decided to see what this SQL thing was all about. With the help of some great free courses, I began to understand the basics. By no means did I become an expert over night, but getting started down this path opened my eyes to many terms and concepts I had not previously considered.
Trust me on this one, if you’re great at building models in Excel, SQL is not beyond your grasp. It might look intimidating, but it’s all a matter of reframing the analysis you already know how to do. Invest the time, and you’ll get the hang of it!
2. Set up your own testing environments — they’re usually free
As the old saying goes, the best way to learn is by doing. And the best way to get to doing is by setting up a low stakes sample environment to play around in. Think back to your finance training — was the first model you were ever handed part of a live transaction? No! It was likely in a classroom with dummy numbers and fake companies. In these environments, we’re free to make the mistakes that we certainly don’t want to make when it counts.
My advice would be for you to set up a workspace within Google’s ecosystem:
- Start by going to Google Cloud Console to create an account
- Next, you can go to a resource like data.gov to find a free dataset that interests you. Download that file as a CSV, and tab back to your Google Cloud instance
- Once you’re there, scroll down to BigQuery and head over to the SQL workspace
- Within SQL workspace, you can click the ‘Add’ button to upload your CSV. Here you’ll just have to create a new Dataset and Table name, and allow BigQuery to auto detect the schema. That sounds fancy, but it really just means specify what data types are within each of your columns (strings, integers, etc)
- Finally, you can click ‘Create Table’. Once that table is created, you should see it on the lefthand side of your console. Click the three little dots next to that table name, hit ‘Query’ and you’re off to the races!
If you’ve followed all these steps, you’ve just configured your first dataset in a cloud data warehouse! Things get more complicated, but these basic steps will take you a long way in your journey. Build some fun queries, and now you have a great personal project to speak to during your interview process.
3. Take advantage of Google and Chat GPT — they can be your best friends
Yes, I know, this one seems really obvious. As a former Finance professional, you probably remember hearing this advice in your analyst days but it bears repeating — almost any question that you have has already been asked by someone else on the internet. There are thousands of forums, blog posts, and reddit threads covering everything from the general questions (how do I get started in SQL) to the extremely specific ones (troubleshooting particular error messages, code snippets for exact use cases, etc.).
If you don’t see what you need in Google, or you still need a bit more specific help, I can’t recommend Chat GPT highly enough. There’s a lot to be said about the role AI will play in the future of Data (and in the world in general) but I won’t get into that here. What I will say is that while imperfect, Chat GPT is a great tool to help trouble shoot and double click into your particular questions. Try a few prompts like “Can you help me write a sample query that joins between two hypothetical tables” or “How do I get started configuring a BigQuery Instance?” and you’ll see what I mean. Oh, and pay the $20 / month for the latest model — it’s worth it.
4. Leverage your network, and be specific about the type of role you’re looking to transition to
Now that you’ve got some data competency, the next big step is putting yourself out there to find the right opportunity. When I was trying to leave Banking, the biggest mistake I made at first is that I was casting too wide of a net. I knew I wanted to do something different, but I wasn’t sure what else was out there. As a result, in many of my initial conversations, I wasn’t really giving the people in my network much they could use to help me.
If you’ve followed the steps above, you likely have a big head start. Reach out to your mentors, connections, and friends across industries and tell them about the transition you’re hoping to make. Be as specific as possible about what you’re hoping to accomplish next. Do you want to work as an analyst for a business development team at a start-up? Or maybe you’re hoping for a more traditional analytics role at a fortune 500? To the extent possible, try to have these things figured out before you get on the phone. Your contacts might not know much about Data, but I guarantee they know the right people to talk to!
5. Be Confident in your Skills — there is value in your experience!
You might not be the best analyst on your new team from day one, and that’s ok. What you bring to the table from your experience in Finance is a strong work ethic, and an understanding of the P&L that many others do not possess. While you’re getting up to speed on the technical details (which I promise, you will learn) you still have plenty to offer your new team.
Be the person that double checks the numbers. If you’re building a dashboard for a member of the business team, don’t just check the box — try to anticipate what they’re going to want to see even if they haven’t asked for it yet. You’d be surprised at how many times I’ve seen someone on the Data team bring a brilliant analysis to the table, only to have all their work undermined by an inconsistency in the numbers or a lack of confidence in their accuracy. Go the extra mile!
Conclusion
Getting a sought after job in Finance is not easy. The fact that you achieved this means you have a lot of qualities any successful company will want. By taking some initiative and leveraging free resources on the internet you can get a great head start on a career transition to Data without breaking the bank for an expensive bootcamp.
If you liked this article, you might enjoy one of our other blog posts on breaking into the world of data analytics. And if you have more specific questions, or need help with analytics on a project of your own shoot me a note at james@southshore.LLC .
About Us
South Shore Analytics (SSA) is an Analytics Consulting Firm, co-founded by James Burke and Nick Lisauskas, both highly skilled professionals with more than a decade of invaluable experience in the field of Analytics. Their shared passion for data-driven insights and business optimization led them to establish SSA, aiming to provide top-notch services to various businesses, irrespective of their size or stage of development.
