Changing Careers — from Banking to Tech

Just 6 months ago, I was slaving away day and night at a prestigious investment banking firm, churning out requests for clients, usually on unreasonably short notice.

Now, I work at a fintech startup where I enjoy my job, have time to pursue my side projects, and continue to learn every day. Making the switch required 3 months of prep (add more if you haven’t had a lifelong interest in the product / tech of the companies you are applying for) and 1–2 months of interviewing (although I got extremely lucky; I have heard from others that finding the right fit could take 6–12 months).

As I continue to grow into my role, I realised that advice from someone who was 6 months ahead of me in the career change process would have been invaluable when I first started thinking about transitioning.

Based on the kind of role you want to target, you might need to employ different strategies but moving from a technical M&A role into a more commercial startup role, I found that undertaking the steps below served me well.

Building up Technical Skills

Personally, I think this is the most important and understated aspect of interview prep. Startups (and tech companies broadly) operate very differently from banks / other financial firms especially if you are used to the structure of a graduate training programme! The best ways to prepare yourself are:

(i) Understanding the mathematical concepts associated with the role you are interested in: for me, this involved understanding customer LTV, conversion, retention and churn etc. You should aim to be able to create simple models (in Excel / Python / tool of your choice) to demonstrate your understanding of these principles and therefore a working knowledge of the tool of your choice is also important. Be ready for some paired exercises where you work with your interviewer to build simple models.

(ii) I found that a working knowledge of SQL served me extremely well. Most tech-enabled startups will use some relational database to store customer data and the easiest way to understand the business when you first join is by querying the database, reading queries others have written to understand which metrics are most important, how to calculate them, and how to optimise them. Depending on the role, you might or might not need to do a paired SQL exercise but it is definitely a plus if you understand it. A great course to build a fundamental understanding is linked ahead: Master SQL for Data Science.

(iii) Bonus points if you have a working knowledge of Python particularly the more commonly used libraries like Pandas, Scikit-learn, Matplotlib but don’t worry too much if you don’t. If needed, you can always pick these up relatively quickly.

Understanding Product

This is another aspect of the prep which is difficult to master as by design, you should interview for a broad range of roles. However, what really sets candidates apart is whether or not they understand the product of the company. You should aim to use the product (where possible) especially if it is an easily downloadable app and write some thoughts about how you found the user experience, what you liked and disliked and so on.

An easy way to set yourself up for success here is to use a wide range of apps and products generally to develop your product sense and if you are lucky (as I was in my current role), you might have been using the product of the company you end up working at before you even apply for a role.

Preparing for Situational Interviews

In all interviews, there will be aspects of situational questions asking you how you dealt with problems, technical challenges you solved, failure stories and how (if) you bounced back etc. Most candidates prepare for these but the best ones follow a structured approach to answering these questions (think STAR or some other framework you like).

I found that having a few of these stories on hand that you can use for different kinds of questions is very helpful as you will likely be asked different questions by different interviewers at the same company and you don’t want to recycle one story in 5 different ways. Another tip is to look at the job requirements and prepare your stories to address the requirements. For instance, if the job specifies advanced financial modelling skills, you might want to address the question on technical problem solved by talking about that time you crunched out a model overnight for an unreasonable client request.

Acing the Take-Home Task

For most junior and mid-level roles, you will be asked to complete a take-home task which will likely involve some data analysis and then presenting your findings to an interviewer or a panel of interviewers. I can’t stress enough how important it is to prepare well for this part as even if you get invited for further interviews and you haven’t aced this round, you might get overlooked for someone who has done extremely well.

I have a method to complete these tasks and it involves setting a whole day aside to do initial exploratory data analysis and really digging into the problem to make sure I understand it from every angle before writing the code / building the model which goes into the presentation.

Then I sketch out (in powerpoint) pieces from my analysis that I think should go into the presentation to explain my solution before sending it off to a great designer I use for my consulting projects. This really makes a huge difference as a well-designed presentation demonstrates to interviewers that you care enough to make an effort. If the first task that you do for your potential employer is half-ar**d, it is difficult for the interviewers to see you thriving in the role.

Even if you do all the above, there is a huge amount of luck involved in finding a role you are passionate about at a company which is building something you want to see built. With that in mind, apply for as many roles as you can and continue to network and interview even if you are happy at your current role and over time, you will find yourself doing more of what you love!