How I Decided to Change My Career to Data Science

Choosing a career can be difficult, especially when you are already entrenched in another career path. A little over a month ago I was working for a fintech company I had worked for seven years. I had a variety of different roles over my time in the company, most recently doing quality assurance for new features and bug fixes to the product. While there were a lot of great things about my job (flexible hours, great coworkers and management, etc), I was not satisfied. Quality assurance was a career that I fell into, and more and more I knew that I needed to get in the driver’s seat and choose a more fulfilling career. To find that career, I had to ask myself a few important questions.

First I had to consider what parts of my current job I did not enjoy. One thing about quality assurance is that there was not much in the way of finding answers. Instead, I typically was given the set of criteria for a new feature, and had to go through the motions to make sure all of those features were in fact there. I wanted more of a sense of discovery in my work; to come up with a question, make a hypothesis, and find out if my inferences were correct. You know, kind of like a scientist. Any career has aspects that an employee will really enjoy and others that are, perhaps, tedious. In QA, the mysteries I found were not solvable. I was not in charge of figuring out what was going wrong, simply that a feature was or was not working. There was no mystery and it made the work increasingly tedious over time. Eventually…the tedium really wore me down.

It was easy enough to figure what I did not like, but next was picking apart the parts of my career that I did like. From my experience in quality assurance, I knew I liked collaborating between teams to make sure everything was air-tight on expectations. Next I thought about how much I enjoyed learning SQL. Before moving into quality assurance, I was tasked with investigating issues on customer accounts, identifying the cause of the issue, and querying the company database to determine if any other accounts were facing the same issue. It was always extremely satisfying to come up with a hypothesis for what was causing an issue on an account and building a query to comb through the database to find other accounts with the same symptoms. From there I would write up a report and detailing what the issue was, what caused the issue, how I identified the accounts, and suggested a fix. I enjoyed getting familiar with the topic and being able to explain in detail my thought processes on identifying and resolving issues.

So I knew I liked sorting collaborating with other teams to set expectations on what we all wanted, sorting through data, and explaining my findings in detail. In an abstract sense, I knew that companies used data to inform decisions, and that somebody had to manage that, but I did not know what that position would be called or if that was even a large industry. Honestly I didn’t even really know if that was a single position, or an auxiliary role in other positions (like a system administrator cleaning and organizing data as a part of their other duties, or product managers reviewing data as a part of their job). Then I saw a list of growing careers and saw data science. The more I read about it the more it seemed to make sense for me. Not only were the parts of my career that I enjoyed their own position, but there was so much more to it that I did not expect.

Finally, I had to look into the hard skills needed and decide if I was willing and able to learn them. It is one thing to get excited about using skills I had learned in a new career, but there was a lot I needed to learn before I could even get my foot in the door for data science. Most prominently, I would have to increase my knowledge of programming, statistical analysis, and machine learning. While it was great that I knew SQL, and I had taken beginner courses in Python and Java in the past, a data scientist needs a much larger programming skill set. The last time that I did statistical analysis, I was in college. I had no idea where to even start for machine learning. But I had enjoyed learning programming and statistics in the past, and was excited by the idea of machine learning.

All that was left was figuring out how I was going to get these skills. I had met people with good experiences with coding boot camps, and saw that there were similar programs aimed specifically at data science. After taking a practice course at Flatiron School I was all in. I am ready to take the jump and become a data scientist.