This blog is contributed by Burtch Works’ data science & analytics recruiting team
No doubt about it – data science and analytics jobs are hot these days! As recruiters that focus on positions in these fields, we’ve noticed an increase in the number of professionals looking to transition from other fields into more quantitative roles, or into new roles within these fields.
It can be tough to know where to start, so we wanted to put together a quick guide with our advice on common skills and tools to learn, as well as how to pursue a new position in analytics or data science.
Evaluate Your Current Skillset and Where You Want to Be
Transitioning to a new position is not always as “simple” as picking up a data science certification or completing an 8-week bootcamp. Regardless of how rigorous the training in these programs is, many companies are looking for professionals that already come to the table with hands-on professional experience in an analytics or data scientist role.
1. Before you choose a transition route, first evaluate where you fall on the quantitative spectrum already. For example:
> Are you on the lighter side quantitatively or transitioning from a less-technical field?
> Or do you already possess a strong statistics or computer science background, and just need to sharpen your Python coding or understanding of machine learning techniques to land a data scientist position?
2. Next, take a look at where you’d like to be, and be realistic.
> Do you see yourself hands-on with the data day-in and day-out?
> Or could you be a more client-facing professional who can speak to the analytics but is less hands-on?
> Something else?
Look through some analytics and data science job descriptions and think about what you’d like to be doing, as well as how many skills you’ll need to learn to get there. This post compares Master’s programs, MOOCs, and bootcamps as learning approaches for aspiring data scientists.
Common Tools and Skill Areas for Analytics & Data Science Roles
Depending on the types of roles and industries you’re targeting, the required skills will vary. However, there are a few categories to look at to make sure you’re hitting all the basics.
1. Data mining / querying tools
SQL, mongoDB, Hadoop. Cleansing and manipulating the data and retrieving what you need for analysis from a data warehouse.
2. Analytical tools
SPSS (often used for survey), R, Python, SAS. If you don’t have time or ability to learn more than one, err on the side of Python or R. They’re both open source, which is where more companies are heading, and since they are free programming languages (as opposed to licensed programs like SAS), they’re more accessible.
3. Statistical methodologies
Regression, time series, cluster analysis, survey methodologies, or the more advanced machine learning techniques.
4. Advanced programming languages
Computer science skills and coding abilities, usually C++, Java, Tensorflow.
5. Ability to communicate and distill these technical concepts
Are you able to communicate your findings to a less-technical audience? Most data scientist and analytics roles will require some amount of collaboration with other business units (such as Marketing or Sales), so being able to translate your findings to someone who might not have as technical of a background is a key skill!
6. Business acumen
How does your analysis tie back to the problem at hand? What strategic recommendations can be made that fall within the scope of work? Are you able to develop actionable insights from the data? We dig into how to further develop and demonstrate this skill even more in this post.
How to Transition into an Analytics or Data Scientist Role
If you have some technical experience, such as an engineering or physics background, or, once you’ve started working with smaller amounts of data and are looking to expand your capabilities, there are two main ways that you can look to make the transition: within your current organization or starting as an entry-level or junior analytics professional or data scientist with a new organization.
Before choosing any of these options, we suggest that you start to network with the organization you are particularly drawn to. 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.
1. Switching roles within your current organization
The skills and tools needed for an analytics or data science position will vary by organization, so be sure to research some of the roles that you might be interested in to see what they’re looking for! Maybe you have some coding 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 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 analytics or data science space/function. This might involve an internal transfer to another team or possibly sitting in on a data science project.
What does this look like?
An example might be a quant analyst or engineer with strong technical skills in Python or other 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.
Or – if you’re working with smaller datasets in programs like Excel, find an opportunity to beef up your statistical chops – basic regression, clustering, predictive modeling, etc. by learning from statisticians or data science counterparts. Once you have the basics, you can use resources like Kaggle to practice working with larger datasets. This might take a little more effort if you don’t have the statistical/mathematical background to start.
Because applied knowledge is so crucial to quantitative positions, many companies will want to see work experience on data science projects with the necessary tools before hiring someone into a non-entry data scientist or analytics 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 position, which typically carries less expectations of professionally applied data science skills.
2. Applying for an entry Analytics or Data Scientist role at a new company
It is important to set your expectations about this realistically. Regardless of the hiring demand, the analytics and data science fields still require rigorous technical training, and can be incredibly competitive. If you don’t have applied experience (experience gained on the job, not just from a training program or Kaggle competitions), you will be working at a disadvantage during your job search.
What does this look like?
While advanced degrees are the most compelling and suitable option if you don’t already have a quantitative background, such as a Statistics or Analytics program from a reputable university, sometimes this isn’t a viable option. Maybe consider a bootcamp alternative – Metis, Galvanize, General Assembly, etc. Make sure you do your research on these – placement rates, target companies, skills acquired, alumni background. Bootcamps tend to be better options for those with some prior knowledge in mathematics and coding.
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 prefer to hire interns into their entry positions, rather than looking for a higher-paid professional who already has work experience but not necessarily in an analytics or data science position.
Be realistic about what this option might entail
If you’re already several years into your career and are unable to transition within your current organization, you may need to make peace with the fact that the compensation you will be offered in an entry data science or analytics role may be lower than your current compensation, 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 you may need to take a step back in compensation for the time being.
3. Using your current skills as a stepping stone at another company
We 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 or analytics roles within another company that might be more accommodating to you moving onto or within their quantitative team.
We hope this information was helpful and gives you some ideas on how to start your learning journey – best of luck!
Want to learn more about data science and analytics career planning? Watch the webinar recording below to hear our recruiters’ best advice on interview preparation, tools and skills to learn, salaries, and more!