This post is contributed by Burtch Works’ analytics recruiting team.
No doubt about it – data science jobs are hot these days! As a recruiter that focuses on analytics roles within financial services, I’ve noticed an uptick in the number of quant analytics or strategic trading professionals looking to transition into data scientist roles. However, this is not always as “simple” as picking up a data science certification or participating in an 8-week bootcamp.
Regardless of how rigorous your training is, many companies are looking for professionals that already come to the table with hands on professional experience in data science. If you have experience within consumer or commercial risk analytics, market risk analytics, or analytics within the trading space, there are two main ways that you can look to make this transition into data science: within your current organization or starting as a junior-level data scientist with a new organization.
1. Transitioning within your current organization
Although the skills and tools needed for a data science position will vary by organization, this blog post goes into more detail about the most common technical skills companies look for, as well as key non-technical requirements. Be sure to research some of the roles that you might be interested in to see what they’re looking for! Maybe you have experience with Python but would like to learn more about Hadoop or Spark, or maybe you want to learn more about machine learning techniques or other methodologies.
Train up with another team
If you’re able to, a great way to make the transition to data science is to train up with another team in your current organization. In many cases, once you have established personal equity within a company (it is clear that they value you and the work that you do), they will understand the advantage to retaining you, and training you into a position that would keep you engaged in the organization.
Talk with your current manager
If you have a good relationship with your manager, this typically entails speaking with the person you report to and starting the conversation about your future and vision of moving into the data science space/function. This might involve an internal transfer to another team that could serve those needs, or possibly sitting in on a data science project.
What does this look like?
An example might be a quant analyst from an investment bank with strong technical skills in Python, and other object oriented programming as well as some experience with time series modeling, finding a way to get exposed to more machine learning techniques, like neural nets and decision trees. This might be possible to do within the environment you have at your current company.
Because applied knowledge is so crucial to a data science hire, many companies will want to see work experience with the necessary tools and data science projects before hiring someone into a non-entry data science role.
If you’re unable to transition in your current company, then your alternative may be to train yourself on as many skills as you can on your own and apply for an entry data science position, which typically carries less expectations of professionally applied data science skills.
2. Applying for a data scientist role at a new company
It is important to set your expectations about this realistically. Even with the increased demand the data science space is incredibly competitive, and without applied experience you will be working at a disadvantage during your job search.
Why this can be challenging
At certain times in the year, you will be competing with freshly-graduating PhD and Master’s students who will have experience with not only the relevant technology and business practices that their faculty shares, but may also have significant experience with internship and research programs as well. And speaking of internships, depending on the organization you’re looking to join, they may look to hire data science interns into their entry positions, rather than looking for a higher-paid professional who has work experience but not necessarily in a data science position.
Be realistic about what this option might entail
If you are unable to transition within your current organization, you may need to make peace with the fact that the compensation you will be offered as an entry data scientist will most likely be lower than your current compensation in the banking or trading space, and it may take some time to get back to where you were before. Data scientists, as our salary studies have found, are highly paid, but without applied experience in a data science role you may be accepting an entry data scientist position, which may mean a step back in compensation for the time being.
I had said there were two main ways to shift into data science jobs from another financial role, but if neither of those previous options work for you, don’t fret! There is a third, although slightly less direct, route if neither of those options are available to you. You can take a look at the techniques that connect over to other companies and industries, and use them to shift into data science within another company that might be more accommodating to you moving onto a data science team.
What does this look like?
For instance, many insurance companies value time series modeling for pricing premiums, measuring risk, and figuring out ways to target consumers more effectively. There are many organizations looking for strong object oriented programmers for products related to credit scoring, real time decisioning, and fraud.
Before choosing any of these options, I would suggest that you start to network with the organization you are particularly drawn to, because you might be able to develop a mentorship with someone from that organization. Learn as much as you can about tools, skills, methodologies, industry news, and technological developments, because that knowledge will be to your advantage regardless of where your transition happens.
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