The different roles in Data Science. Who does what? What composes a Data Team?

“The machine approach is essential, because the data is so massive, but you still need a skilled human to interpret what the data is telling you and figure out what it means in a business context.”

Welcome to the Data for Everyone newsletter! On this week's entry we'll be discussing the different roles that you can find in Data Science.

In today's digital era, data roles are continually expanding... There's up to 62 data-related roles on the market (Don't worry this won't be an essay discussing all of them), but today we'll discuss the main roles that you can find on any company.

Before we dive deep into discussing the roles, it's important to mention that there's no one-size-fits-all approach when it comes to define the responsibilities of each role as every company has different needs and responsibilities for each data role.

For example, let's compare these two LinkedIn job posts for a Data Scientist role by two different companies: Meta & Amazon.

As you've probably observed, though both companies are looking for a Data Scientist, they have different qualifications, responsibilities & requirements. What this proves is that though each role requires the data professional to have a wide variety of skills, there's no black & white when it comes to define what are the exact responsibilities of a data role, as two companies have different needs for the same role. This also means that data professionals ought to be versatile and need to have the capability of adapting to the different needs that their organization has.

Now that we've clarified this, let's go ahead and analyze the following roles: Data Scientist, Data Engineer, Machine Learning Engineer, Data Analyst & Business Analyst (The main roles in Data).

DATA SCIENTIST

Think of a?data scientist?as a new?generation?of analytical data?experts with?the technical skills to solve complex problems.?They are partly mathematicians, partly?computer?scientists?and?partly trend observers. They are?also a sign of the times. Data scientists?weren't?on?much radar?a decade ago, but their sudden popularity reflects how?companies?think about Big?Data today.

Data scientists must understand the challenges of?the?business and?provide?the best solutions using data analysis and processing. For?example,?they are expected to perform predictive?analytics and perform thorough?analysis?of "unstructured / disorganized"?data to?provide useful information.?They can also do this by identifying trends and patterns that can help companies?make?better?decisions. One of?today's?most in-demand?professionals,?data scientists rule the?foundation?of number crunchers.?

Data?scientists?take on many of the same responsibilities as analysts, but?are?also responsible for building machine learning models and working with algorithms to make accurate predictions based on?the?collected?data, ultimately?making?the work of data analysts?a little?bit more. simple.?Of course,?it's?always?helpful?to know how?analytics fit?into the?bigger?picture, and successful?data scientists?have a solid understanding of handling raw data,?analyzing,?and sharing insights in compelling?ways.?Since the role?is usually?more independent, motivation and curiosity go a long way for these professionals.

• Responsibilities:?Analyzing data, building and training machine learning models to make reliable future predictions, express insights /results & communicate with stakeholders.
• Programming languages required:?Python, R, SQL, SAS, Hadoop, Tableau/Power BI.
• Tools/skills required:?Everything required from a data analyst, plus strong foundations in math, analytics and computer science, knowledge of machine learning methods, statistical models, advanced?data science?programming and familiarity with Apache Spark.
• Growth potential:?Data Scientists may move on to become a senior data scientist, while some decide to take the path to become a machine learning engineer or a chief data officer.
• Top industries:?Big Tech, Consulting, Corporate, Healthcare, Telecommunications, Energy, Automotiv.

How to Become a Data Scientist?

To become a data scientist, you have to be an expert in R, MatLab, SQL, Python, and other complementary technologies. It can also help if you have a higher degree in mathematics or computer engineering, etc.

DATA ENGINEER / DATA ARCHITECT

Big data engineers develop, maintain,?test?and evaluate big data solutions within?organizations. They are also involved in the design of big data solutions,?thanks to their?experience with Hadoop [-] based technologies such as MapReduce, Hive,?MongoDB?or?Cassandra.

To support big data analysts and meet business requirements?through feature?customization and?optimization,?big data engineers configure, use, and program big data?solutions. Using various?open source?tools, they?design "highly?scalable distributed?systems".?They?need to?integrate?the?data processing and?management infrastructure.

It is a?very?cross-functional role. With?multiple?years of experience, responsibilities in development and operations; policies,?standards?and procedures; communication; business continuity and disaster recovery; coaching and mentoring; and research and evaluation?are on the rise.

• Responsibilities:?Processing data provided by a company’s Data Analyst using machine learning algorithms developed by the Data Scientist to glean insights that will ultimately drive business decisions
• Programming languages required:?R, Java, Python, C++
• Tools/skills required:?Basics of algorithms and data structures, Hadoop cluster management, stream'processing solutions, big data querying tools, NoSQL databases, frameworks and ETL tools, Cloudera, SparkML, etc...
• Growth potential:?The career path would be Data Engineer to a senior Data Engineer or transitition to Business Intelligence roles like BI Architect or Data Architect
• Top industries:?Big Tech, healthcare, sports, retail, government, healthcare

How to Become a Data Engineer?

Firstly, you must have a sound knowledge of some of the technologies like Java, Python, JS, etc. Secondly, you should have a strong grasp of statistics and mathematics. Once you have mastered both, it is a lot easier to crack a job interview

MACHINE LEARNING ENGINEER

While?data scientists build the?machine learning models?of an organization?and?data analysts?determine?what?data is?worth investigating, it is the machine learning engineer who takes care?of the?algorithms?and applies?them?to?data sets. Typically,?the ultimate goal?of people?in this?position?is to eventually?develop?artificial intelligence.?The job involves a lot?of?trial and error,?so?perseverance?and resilience are?important success factors.?In addition,?this field also demonstrates?a?strong?understanding of?the time required?to apply?different approaches.

Machine learning engineers are in high demand today. However, the?professional?profile?presents?its?own?challenges.?In addition to?in-depth knowledge?of?some of the?more?powerful technologies such as SQL, REST?API, etc.,?machine learning engineers?should?also perform?A / B?testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc. This is a highly?desirable?role and can?underpin?some of the?more user-centric?applications the internet has to offer today,?like Spotify's recommendation?systems or Discover Weekly?playlist.

• Responsibilities:?Processing data provided by a company’s Data Analyst using machine learning algorithms developed by the Data Scientist to glean insights that will ultimately drive business decisions
• Programming languages required:?R, Java, Python, C++
• Tools/skills required:?Strong communication paired with an understanding of data structures, vectors, matrices, derivatives and integrals, as well as statistical concepts and probability theory
• Growth potential:?Many Machine Learning Engineers progress to become more specialized in deep learning methods, while others transition to machine learning researchers or leads on data engineering teams
• Top industries:?Big Tech, healthcare, financial services, retail, government, transportation

How to Become a Machine Learning Engineer?

Firstly, you must have a sound knowledge of some of the technologies like Java, Python, JS, etc. Secondly, you should have a strong grasp of statistics and mathematics. Once you have mastered both, it is a lot easier to crack a job interview

Data Analyst

Unlike data scientists, data analysts?tend to be generalists.?They play a?variety?of roles,?ranging?from?collecting?massive amounts of data to processing and summarizing?them.?This role can have a lot of freedom in?how?you?make an?impact. Any trend or?idea?you identify and convince?decision makers?to act?on?is a win.?The?potential downside?to?this freedom is that you?often don't?have?to follow?a single?well-defined question, which can lead?to lack of focus or frustration?at?not?knowing?what to work on. Developing a sense of priorities and the ability to identify?high impact?projects?are?key?skills.

The word?"analyst"?tends to have a?newer?connotation and?as such?many companies (especially in the?tech industry)?use?"data scientist" instead.?My advice would be to not pay?too?much attention to the word used and instead focus on the job description,?the?team,?the company and?of?course?the potential salary.

• Responsibilities:?Accessing and cleaning data, performing statistical analysis, visualizing and communicating the results
• Programming languages required:?Python, R, SQL, Tableau
• Tools/skills required:?Data science programming, probability and statistics, collaboration, communication
• Growth potential:?Many Data Analysts go on to become senior analysts or take on a management role at larger companies with data teams
• Top industries:?Finance, insurance, gambling, retail banking, consumer products, healthcare, energy

How to become a Data Analyst?

SQL, R, SAS, Python are some of the sought-after technologies for data analysis. So, certification in these can easily give a boost to your job applications. You should also have good problem-solving qualities.

Business Analyst

The role of business analysts is?somewhat?different from other?jobs in?data?science.?While they have a good understanding of how data-oriented technologies work and how to handle large?amounts?of data, they also separate high-value from low-value data. In other words, they identify how Big Data can be?coupled with?actionable business?information?for business?growth.

Although many of their?duties?are similar to?those?of data analysts, business analysts are experts in the?field in which?they?work.?They try to narrow the gap between business and IT. Business analysts?often offer technology-based?solutions to?improve?business processes, such as distribution or?productivity.

Organizations need these?"information channels"?for a?myriad?of things?like?gap analysis, requirements gathering, knowledge transfer to developers,?scoping?using optimal solutions, test preparation, and software documentation.

• Responsibilities:?Use data-driven insights to clearly communicate initiatives throughout entire organizations, often acting as the intermediary between a company’s business and tech teams
• Programming languages required:?SQL, Tableau, Excel
• Tools/skills required:?Understanding of business processes, data visualization tools, listening and storytelling, data modeling
• Growth potential:?With experience, many Business Analysts take on a leadership title or move on to more senior roles in product management
• Top industries:?Telecom, utilities, real estate, healthcare, government, pharmaceuticals

How to Become a Business Analyst?

Business analysts act as a link between the data engineers and the management executives. So, they should understand business finances and business intelligence, and also the IT technologies like data modeling, data visualization tools, etc

I hope that after reading this article you've gained a better understanding of the main roles in Data Science and which one could relate to yourself and your set of skills. Just remember that you have all the time in the world to make the transition to Data!

“In Data Science if you want to help individuals, be empathetic and ask questions; that way, you can begin to understand their journey, too.”

-Alfredo S.