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10 data science job roles you must know

Data science job roles

A plethora of different data science roles have come our way in the past few years, and it’s really DIFFICULT to get a general understanding of the differences between them and of the skills they require. To compound matters, the fact that these different data science roles are often given different titles, some of which are truly imaginative, doesn’t help either. Fortunately, the data science industry’s job market is in great demand  today, which implies that one way to get a better understanding of the meaning behind different data science roles is by having an in-depth  look at all job offerings, their description and their skill requirements. These listings often go into painstakingly great detail and help to answer questions such as “How similar or different are certain roles?”, “What technology set of skills  do I need to master?” and “What is the mindset that I require while I’m accepting this job?. In  today’s post, we take a look at this plethora of data science job postings in an attempt to demystify these cool-sounding and playful job titles into a comparison of different data science related careers.  Data Scientist One of the hottest job titles that you can proudly flaunt on your business card, is that of a data scientist. Data scientists are as rare as unicorns and get to work every day with the mindset of a curious data wizard. They master a whole range of skills and talents; from being able to handle the raw data, analyzing that data with the help of statistical techniques, to sharing their insights with their peers in a compelling manner. It’s little wonder that these profiles are highly wanted by companies like Microsoft and Google. Advanced analytics professional An advanced analytics professional would typically perform  simulations, predictive analytics, prescriptive analytics, and other forms of advanced analysis. He /she would differ from data scientists because he/she would not work with exceptionally large data sets or with unstructured data. Data Analyst Data analyst job listings encompasses a large spectrum of responsibilities, right from  creating systems that enable business users to gain insights, to ensuring data quality and governance, and to the performance of actual data analysis. But the skill sets are similar. Usually, these professionals fit into the same category as advanced analytics and data scientist professionals, since they all can analyze data. However, data analysts may be considered more junior-level professionals who are still generalists and can fit into several different roles within an organization. Data Engineer Making the jobs of data scientists and data analysts easier, is what the data engineers do; by working quietly behind the scenes. These technology professionals have an in-depth  knowledge of Hadoop and bigdata technologies such as MapReduce, Hive, and Pig, NoSQL technologies, SQL technologies, and data warehousing solutions.  It can be said their jobs are to build the plumbing — data pipelines that clean, aggregate, and organize data from different sources, and then load them into databases or data warehouses. Data engineers are not the ones that  analyze the data. They are the ones who create the software infrastructure that keeps the data flowing and processing so that the data can be analyzed by other professionals. Business analyst Tasks that are very similar to those performed by data analysts can be performed by business analysts. However, business analysts would typically possess specialized knowledge of their business domain, and they then apply that knowledge and analysis specifically to the operation of the business. For example, they could put to use their analysis to recommend improvements to business processes. Database Administrator A database administrator is responsible for all things related to the  monitoring, operation and maintenance of databases; often SQL or other relational database management systems.  Installation, configuration, defining schemas, training users, and maintaining documentation is what their tasks include. Business Intelligence Professional Those adept at using OLAP tools, reports, and dashboards to look at historical trends in data sets are business intelligence professionals. Business intelligence can include data visualization. Popular business intelligence platforms include, Qlik, Tableau and Microsoft Power BI. The statistician The historical leader of data and its insights – the statistician! Replaced by more exotic sounding job titles, and though often forgotten; the statistician represents what the data science field stands for – gaining useful insights from data. Armed with a strong background in statistical theories and methodologies, in addition to  a logical and statistics oriented mindset, the statistician harvests the data and converts it into information and knowledge. Statisticians have the capability to handle all kinds of data. Further, thanks to their quantitative background, modern statisticians can master new technologies in no time and use these to enhance their intellectual capacities. Statisticians bring  mathemagic to the table, and their insights can radically transform businesses. Data Architect The importance of a data architect’s job is quickly increasing, with the rise of big data. The data architect creates the blueprints for data management systems to integrate, centralize, protect and maintain the data sources. The data architect masters technologies like  Pig, Spark and Hive, and is required to be on top of every new innovation in the industry. The Machine Learning Engineer There exist a number of companies for whom their data or their data analysis platform is their product. If so, the data analysis or machine learning going on can be quite intense. This would probably be the ideal job for someone who has a formal mathematics, statistics, or physics background and wishes to continue down a more academically oriented path. “Machine Learning Engineers would focus more on producing great data-driven products, than they would,  giving answers to operational questions for a company.” Companies that fall into this group could be consumer-facing companies with voluminous amounts of data or companies that  offer a data-based service.

Manu Jeevan

Manu Jeevan is a self-taught data scientist and loves to explain data science concepts in simple terms. You can connect with him on LinkedIn, or email him at manu@bigdataexaminer.com.
Manu Jeevan
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