Demystifying Data Job Roles: A Guide to Finding Your Place in the Data World

This article aims to demystify the different job titles for data science and machine learning based career paths. We would look into some job titles such as Data Analyst, Data Scientist, Data Engineer, and Machine Learning Engineer; and learn about the job & responsibilities of each title along with the skills/qualifications required and estimated salary earned in different job titles.

DATA ANALYST

As defined in Wikipedia: Data analysis is a process of inspecting, cleansing, transforming and modeling data to discover useful information, informing conclusions and supporting decision-making. Data analysts collect, process and perform statistical analysis on the given data.

Responsibilities of a Data Analyst

Cleaning and organizing Raw data.
Analyzing data to derive insights.
Creating data visualizations.
Producing and maintaining reports.
Collaborating with teams/colleagues based on the insight gained.
Optimizing data collection procedures.

Skills required to be a data analyst

A degree in the following subjects is beneficial in developing a career as a data analyst:

Mathematics
Computer Science
Statistics

Furthermore, additional skills required to be a data analyst are:

Statistical Programming
Programming Languages (R/SAS/Python)
Creative and Analytical Thinking
Business Acumen — Medium to High preferred
Strong Communication Skills.
Data Mining, Cleaning, and Munging
Data Visualization
Data Warehousing
Data Story Telling
SQL Databases
Database Querying Languages
Advanced Microsoft Excel.

Average Annual Salary of Data Analysts

An entry-level data analyst may receive an annual salary anywhere between $40,000 — $80,000 while experienced analysts can expect to receive between $65,000 — $120,000 approximately.

DATA SCIENTIST

“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician”. — Josh Wills on Quora

At the core of data science, Data Analysts and Data Scientists have the same job, that is, getting valuable information from the data. Nevertheless, they differ largely in their job roles & responsibilities.

Data Analysts derives insights from historical data and answers questions such as “What is the cause behind decreased sales?”, “What is the reason behind a particular products failure?”, and shares it with the concerned people for informed business decisions. On the other hand, Data Scientists, are more forward-looking people. They are more concerned with using these insights combined with Machine Learning, hypothesis, Statistical Tests, A/B testing for further development of products. They are more into asking questions such as “Will a larger motor in my product create more demand for it?”, or “Will targeting a particular market to enter into help me grow my company?”.

The above is the general difference between the roles of Data Analyst and a Data Scientist, but it may not always be the case since data science is still new and is far from being standardized. You can sometimes have data scientists doing basic analysis work and data analysts performing ML modeling.

Regardless of the titles, these two career profiles are the most important ones that are being sought after by employers when it comes to analytic roles in data science, and therefore should be our targets when job searching and thinking about the best fit

To summarize, Skills required to be a Data Scientist include that of Data Analysts, Mathematics, Machine Learning, Deep Learning, Reinforcement Learning, Hypothesis Testing, Subject Matter Expertise (SME), Intuition.

Salary of Data Scientists

A Data Scientist’s annual salary ranges anywhere from $80,000 to $160,000 depending upon the experience and subject matter expertise.

DATA ENGINEERS

Gordon Lindsay Glegg once quoted, “A scientist can discover a new star, but he cannot make one. He would have to ask an engineer to do it for him.”

So, the role of the Data Engineer is really valuable.

Data Engineers are people who are in charge of the delivery, storage, and processing of data. The main task of data engineers is to provide a reliable infrastructure for data. If we look at the AI Hierarchy of Needs, data engineering occupies the first 2–3 roles in it: Collect, Move & Store, Data Preparation.

Responsibilities of Data Engineer

Scrape Data from the given sources.
Move/Store the data in optimal servers/warehouses.
Build data pipelines/APIs for easy access to the data.
Handle databases/data warehouses.

Skills required to be a Data Engineer

Strong grasp of algorithms and data structures
Programming Languages (Java/R/Python/Scala) and script writing
Advanced DBMS’s
BIG DATA Tools (Apache Spark, Hadoop, Apache Kafka, Apache Hive)
Cloud Platforms (Amazon Web Services, Google Cloud Platform)
Distributed Systems
Data Pipelines

Is a Data Engineer more in demand than Data Scientist?

The answer is YES, To explain it, let’s consider an analogy:- A potato chips factory needs lots of potatoes daily to be stored before it can start processing it. But potatoes need to be harvested, cleaned and transported before they can become potato chips. Similarly, A piece of data needs to be first collected, stored, and pipelines need to be made for easy access to data before it can be worked on. Therefore, Data Engineering is the preliminary step in any data related work and it is more in demand than any other data-science based job roles.

Salary of a Data Engineer

Salary of a Data Engineer varies anywhere in the range between $110,000 to $155,000 per year depending on skills, experience, and location.

MACHINE LEARNING ENGINEER

The work of a Machine Learning Engineer is to bridge the gap between Data Scientist’s work and production environment. A Machine Learning Engineer is more concerned with deploying production-ready models. For an example, think of a recommender system machine learning model trained with a data set and has achieved great results, now that model needs to be deployed as part of a company’s website to make the site better and thus, help generate more revenue. But what if that model is not production-ready yet? This is where a Machine Learning Engineer comes into play. Deploying machine learning models to a production-ready environment is an engineering concern that is different from building the model. It involves different types of engineering work such as integrating the model to a software system, optimizing the model for performance and scalability, and re-training it with new data, monitoring, and maintenance the ML system

Thus, Responsibilities of Machine Learning Engineer include:

Deploying machine learning models to production ready environment
Scaling and optimizing the model for production
Monitoring and maintenance of deployed models

Skills required to be a Machine Learning Engineer:

Linear Algebra, Calculus
Probability and Statistics
Programming Languages (R/Python/Java/Scala mainly)
Distributed Systems
Data model and evaluation
Machine Learning models
Software Engineering & Systems design

CONCLUSION

Presuming, now you have a brief idea about the different job profiles. From here, it’s all about selecting a field you are interested to work in and start planning out on building the necessary skill set. The demand for Data-Driven careers are always gonna soar high as more and more data is being generated every single day and there’s a high demand for people in these fields.

All the best for your career and job search in Data Science!