By Braulio Giron, Jr. on July 9, 2019
Great data science jobs don’t simply fall on your lap, even if you’ve mastered SQL, Python, or R, among others, but knowing what your options are can help find a successful career
It’s a great time to be a data science professional! Fueled by big data and AI, the demand for data science skills is growing exponentially. However, the number of skilled candidates continues to grow at a comparatively slower pace. So the high demand remains mostly unmet, and surely one of the reasons you’re reading this now is because you’re among those who’ve realized that now is as good a time as any to consider a career in data science.
But where to start? Learning data science skills can revolutionize your career, but after attending online classes, seminars, and boot camps, finding one’s place in the still relatively new and evolving industry can still be complicated. For one thing, there is so far still no universal definition of “data scientist” or “data analyst” which most companies agree on, so different positions with the same title may require different skill sets. Then, there are a plethora of other commonly-used job titles that involve data science work that you might not come across when just searching for “data analyst” or “data scientist”.
To best navigate your way in the data science job market, it is best to figure out what the career you want actually looks like. Are there certain industries that use data science where you most want to be in? And where can the data skills you presently have be best applied? Answering these questions should be the first step in your data science career journey. Although the answers might seem obvious, it’s worth taking the time to probe deeper and really explore all of your potential options. And while you may not be able to look into every job title that might be used by a company, it can be helpful to know some of the most common ones.
The 'Top' Three: Analyst, Scientist, and Engineer
Data analyst, data scientist, and data engineer broadly describe the different roles data experts can play at a company, and are the titles you’ll most likely come across in your data science job search.
Also known by titles like Data Developer or Data Researcher, Data Analysts are primarily responsible for looking at a company or industry data to address business questions, and then presenting the findings to decision-makers who can act on the findings. A typical example is when analysts study sales data from a recent marketing campaign to assess its effectiveness and identify strengths and weaknesses. This often involves numerous skills, including the ability to not only access the data, but also clean it, analyze it, and then visualizing and communicating the results.
Data analysts typically work with multiple people in a company. On one day, you can find yourself working on the aforementioned marketing analytics. Then on another, you could be helping the CEO pinpoint the company’s growth and gap points in the past year. Qualifications vary between employers, but if intent on working as a Data Analyst, you’re recommended to be proficient in at least the following: intermediate data science programming like Python or ASP.NET MVC 5, intermediate SQL queries, data cleaning and visualization, probability and statistics, and the ability to communicate these to those without such a background.
While capable of doing many of the things that Data Analysts do, Data Scientist is a considerably more senior role because as one you’d also be typically tasked to build machine learning models to make accurate predictions about the future based on past data. While Data Analysts are mostly given business questions to answer rather than asked to find interesting trends on their own, Data Scientists are more of the latter, with more freedom to pursue their own ideas and explore various patterns and trends in the data that management may not have considered looking into.
As a data scientist, you would likely be tasked to assess how changes in marketing strategies could affect your company’s bottom line. This would entail the use of your data analysis skills, as well as having to bank on a solid understanding of both supervised and unsupervised machine learning methods, statistics and the ability to evaluate statistical models, and advanced data-science-related programming skills (such as Python).
Data Engineers, on the other hand, are the ones tasked to manage their companies’ data infrastructures. This role requires a little less statistical analysis, but significantly more software development and programming. As part of a data team, Data Engineers are the ones responsible for building data pipelines and provide analysts and scientists the latest sales, marketing, or revenue data in a usable format.
Focused more on software development, aspiring Data Engineers are required to possess advanced programming skills (like, again, Python) in order to work with large datasets and build data pipelines. Also depending on the industry or company you are considering joining as a Data Engineer, it is recommended to gain familiarity with the specific technologies they often rely on. If joining a large company with a sizeable data team, reach out to that team to get an idea of their company’s tech stack.
Teaching and Training
As mentioned, the need for more professionals skilled in the different aspects of Data Science continues to become increasingly prevalent. This has subsequently opened up opportunities to those with skills and experience in the field to take on teaching or training positions, as schools and other learning institutions have expanded their programs to include data science and other related fields.
As a Data Science Instructor, you have the opportunity to share your expertise in all aspects of the field. Apart from teaching and administering assessments, the responsibilities of such roles include curriculum development, as well as taking professional improvement training and courses to continuously remain updated with the new developments in data science. Teaching data science is also a means to expand one’s data science career, as these can also come by way of part-time positions.
Given the extensive amount of knowledge needed for an instructor position, the requirements to work as a Data Science Instructor is often a little more stringent than other data science roles. These include having earned a degree (or equivalent experience) in Business Intelligence, Data Analytics, Data Mining, Data Science, Big Data, or other data-driven technology, fluency in statistics and manipulation of large data sets; and experience as a training or learning facilitator, among others.
Other Roles in Data Science
There are a variety of other job titles you’ll see that either relate directly to the previously mentioned roles or otherwise be jobs that are completely their own while involving data science skills. Like other data science positions, these roles can vary between companies and overall industries.
Alternatively known as Business Intelligence Analyst, a Market Intelligence Analyst is essentially a Data Analyst who is particularly focused on the analysis of market and business trends. BIs or MIs are employed in just about every type of industry, with companies ranging from start-ups to major corporations.
The role often requires familiarity with software-based data analysis tools (like Microsoft Power BI), which one can learn via online courses or in-house training. Additionally, other data analysts skills are also crucial for business intelligence analyst positions, such as Python or R programming skills.
A role that is considered a subfield within data engineering, Data Architects are responsible for building, designing, and maintaining their companies’ data storage systems. A key component of the job are SQL skills, although most Data Architects are also recommended to have a solid command of other tech skills to accommodate the requirements which vary based on employers’ different tech stacks.
Data Architect is among the more complex roles in data science, with data science often not enough to be employed as a DA. Often, the qualifications also include having taken an I.T. course, experience in the design, maintenance, and administration of NOSQL databases, and the ability to perform Java, XQUERY, XML, Query DSL, CQL and other web technology programming, among others.
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