Ever since the term “Data Science” emerged in the tech scene, there has been a lot of confusion and debate in defining and distinguishing the job role of a data scientist. The data science industry is well known for its diverse range of job titles and aggrandizement of job roles, which makes it difficult for anybody to pin down exactly what people in a data science team do. Most of the data science job roles are little squishy in the way they are defined and involve somewhat overlapping responsibilities. To help you, we’ve deciphered some of the most popular data science job roles that you might encounter when searching for data science jobs –

Data Scientist 

This is one of the hottest and sizzling data science job roles that people today are proud to flaunt on their resume. According to the Harvard Business Review, a data scientist is a high ranking professional with the training and curiosity to make discoveries in the big data world. Ideally, someone who extracts value out of data. He can be an X% software engineer, Y% hacker and Z% scientist and that is the reason why the job role of a data scientist is so convoluted. The percentage values for X, Y, and Z depend on the type of job and the skills required. They tend to be the all-rounders having a diverse skill set, right from the ability to handle raw data, analyzing data using various statistical techniques, to sharing insights through beautiful visualizations.

Job RoleClean, and Structure unorganized Big Data
Personality Trait and PassionRare unicorns who are curious data wizards with an inclination for problem-solving.
Required SkillsPredictive ModellingAnalytics Techniques such as Classification, Regression, Clustering.Experience working machine learning algorithms.Data Visualization and Story-Telling
LanguagesPython, R, MATLAB, SQL, SAS

Data Analyst

If you are looking for a Sherlock Holmes kind of a data science job, then this is for you. Data analysts also referred to as data detectives, are more generalists involved in data munging and analysis. They retrieve, gather, organize, and analyze data to reach valuable conclusions. The responsibilities of a data analyst vary based on the type of data they are working with (social media, healthcare, inventory, sales, etc.) and also on the particular client project. Unlike data scientists who require strong math, statistics, and programming skills, data analysts require moderate math, statistics, and coding skills along with a little business acumen.  A typical data analyst plays a gamut of roles from gathering data, setting up infrastructure, producing reports, spotting patterns, and collaborating with others.

Job RoleCollect, organize, and study data using various statistical analysis techniques.
Personality Trait and PassionA data-intuitive wizard.
Required SkillsAbility to write comprehensive reports.Ability to work with data models and reporting packages.Identify patterns and trends in large datasets.Develop key performance indicators for optimizing algorithms.
LanguagesPython, R, SQL, C/C++, Excel, HTML, and JavaScript

Machine Learning Engineer

A machine learning engineer is primarily concerned with applications involving artificial intelligence. A machine learning engineer is a full-blown software engineer with a primary focus on programming machines to perform tasks and take desired actions without having to be directed to do so. For instance, an example of an application that a machine learning engineer could be working on is self-driving cars. At this point, the role of a data scientist and a machine learning engineer might seem the same but they are not so. A data scientist is basically involved in doing the statistical analysis to identify which machine learning approach works best for a problem, develop the model, and prototype it. In contrast, a machine learning engineer works with the prototyped model developed by a data scientist to make it work successfully in the production environment. Unlike a data scientist, a machine learning engineer must have an in-depth understanding of the predictive models and the underlying math behind them. 

RoleProduce great data-driven products to answer the operational questions of an organization.
Personality Trait and PassionExpertise in using data to train machine learning models.
SkillsData modeling and evaluationApplying ML algorithms and librariesSoftware Engineering and System DesignOperationalizing and optimizing the models.
LanguagesPython/R, Scala, Java, C++, and JavaScript.


Statisticians gather, analyze, and derive conclusions from data. However, the way they collect data is different from how  data scientists do. Statisticians usually depend on smaller and traditional methods of data collection such as surveys, experiments, and polls. Statisticians are usually hired by companies that are involved in public opinion and market research, product development and quality control, forensics, finance, and often by local, state, and federal government agencies.  Unlike data scientists who focus on comparing various methods to develop the best ML model, statisticians focus on improving a single model to best fit the data at hand.

Job RoleCollect, analyze and interpret data using statistical theories and methodologies. 
Personality Trait and PassionThe KING of historical data and its insights
Required SkillsDeep theoretical knowledge in Probability and Inference.Problem-Solving and Analytical Skills.Strong foundation in Statistical theories and methodologies.

LanguagesR, MATLAB, SPSS, SAS, Excel, and Perl

Data Engineer or a Data Architect

Data engineer – A designer, builder, and a manager of big data infrastructure of any organization.A data architect is a data engineer with more experience. The responsibilities of a data engineer vary from organization to organization. In a multinational company, a data engineer is responsible for setting and managing standards for data use, metadata, reference data, and master data while in a smaller organization, s/he might just be responsible for basic database administration duties. The prime responsibility of a data engineer/architect is to define how data is stored, integrated, managed, and consumed by various entities and systems. A data architect ensures that the overall big data ecosystem of the organization is free from any glitches so that data scientists have the best tools and systems for their analysis. A data architect is a close friend of the users of data within the company -like data analysts, data scientists, and anyone else using data-driven applications such as operations, marketing staff, and customer support staff.

Job RoleIntegrate, centralize, protect, and maintain various data sources. 
Personality Trait and PassionA modern data modeler in love with data architecture design patterns.
Required Skills
Data AnalysisKnowledge of ETL and BI toolsData ModellingData IntegrationData WarehousingDatabase Design
LanguagesPig, Hive, Spark, XML, SQL and NoSQL

What Next?  Learn the Skills that Matter for your Dream Job Role

As the data science domain expands and develops further, new job titles and roles are likely to emerge.  Regardless of how much experience and what skills you have, Springboard has a well-designed data program for you, to make sure you learn all the required data science skills to build a data science career you’ll love. Graduates of Springboard’s Data Science Program will have a large, hungry, and rewarding job market available to them, and will be qualified to fill nearly all the data science job roles described in this blog.