5 Most Popular Types of Data Science Jobs

2.5 quintillion bytes of data is produced on a daily basis on this planet. But who is the great being behind analysing most of that data and helping all industries by providing business solutions? Yes, it is a Data Scientist (the unsung highly-celebrated heroes)! It has been declared the hottest job of the 22nd century, but do you know the kind of job that Data Science offers? Well, we are here to pass on the torch of enlightenment to you, curious soul!

But before we start on this endeavour, let us begin with an essential tip — Data Science combines a number of disciplines, including data analysis, statistics, computer science, and machine learning. It can be daunting and overwhelming when you are new to the field, but it is important to remember that different companies expect different roles out of the same profile. This means that a “data scientist” is often used as a blanket term where the JD is drastically different, and the set of skills needed also differ.

Here are some Data science jobs that you could look into:

  • The Data Engineer

With the internet taking over the world, a business is practically nonexistent if you cannot claim to have a google search result to your name. Some companies are lucky enough to get a lot of traffic, but with it comes a whole other problem of having to deal with a lot more data. But what do you do with that data? Well, the data can be your portkey to success, if used wisely. A data infrastructure can be set up to keep the company moving forward. Here, machine learning and heavy statistics are less important than strong software engineering skills. Just remember that in companies that are looking to leverage incessant amounts of data can prove to be less plentiful for mentorship opportunities for junior data scientists.

One can grow exponentially in this area, but the risk of stagnating and flopping are also high if your luck abandons you and you are not given enough guidance.

Professional and Technical Expertise
Informatica 9, Unix Shell Scripting, Pl/SQL
Programming Languages: Java / Python / Scala

  • The Data Analyst

Often synonymous to being a Data Scientist in most companies, your job description or job profile might require you to become a Tableau or Excel maestro, or pull data out of the SQL databases, reporting dashboards, and producing essential data visualisations.

Salaries of Data Scientists & Data Analyst

A technique that has gained popularity over the years is A/B testing- where a randomised experiment with two variants is conducted, A and B; the statistical hypothesis is included in its application, and the two-sample hypothesis test is then further used in the working statistics of the company or venture. Your job might also expect you to analyse the results of an A/B test or take the lead on the Google Analytics account of the company.

Professional and Technical Expertise
Informatica 9, Unix Shell Scripting, Pl/SQL
Programming Languages: Java / Python / Scala

  • The Data Architect

The importance of data architects is increasing by the minute. Their job role consists of creating blueprints for all the management of data systems that a company should integrate, protect, centralise, and maintain. Architects in this field need master technologies like Spark, Hive, Pig, in addition to being at par with the ongoing innovation trends in the industry.

Data architects are expected to be skilled at physical data modelling, logical data modelling, data strategy, data querying languages, data policies development, data warehousing, and identifying and choosing a system that will essentially prove to be the best for addressing data storage, retrieval, and management.

  • The Machine Learning Engineer

The focus of Machine Learning Engineers is often placed on producing extraordinary data-driven products more so than answering operational questions for a company. Their attribute is not to know how algorithms work but how to use them. If one knows how to wrangle code, they will be able to churn and cp=omb through the various datasets and be able to find exactly what they seek.

It can prove to be an ideal situation for someone who has delved into mathematics, statistics, or physics formally, and still have the same zeal to pursue a more academically inclined path. Roughly, the role of a  machine learning engineer includes Optimising solutions for performance and scalability, ensuring a functional data flow between database and backend systems, and implementing custom machine learning code.

  • Statistician

While all the aforementioned roles cheerfully explore data, there is this one role which behaves like the damp towel in the party- that is of a statistician. That is not to say that the role is in any way boring, but because these are the people who help the company make decisions beyond the data. There is more to launching your machine learning system than it has worked perfectly in a particular dataset— like, how it works when it runs in production etc. This is where the skills of a Statistician come into play.  They help make decisions and reach conclusions safely when one does not have all the facts perfectly aligned.

The field of Data Science takes a lot of “training and curiosity” to be able to make sound discoveries in the Big Data. With the prediction of data reaching a staggering 40 Zegabytes by 2020, there will definitely be a shortage of trained professionals who deal with that data. Board the train of Data Science, and you might just find yourself getting rewarded for saving the world with how you manoeuvre about data!

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