Top 10 Data Science Career Options Shaping our Future

Today, the entire world runs on data or information by applying the infinite power of the internet and technology. As such, Data Science is one of the most promising fields of this era. It includes data collection, manipulation, storage, management, and analysis which supports organizations to make business decisions. For the past few years, Data Scientist has been ranked as the top most jobs in the U.S.by Glassdoor. According to a report by the U.S. Bureau of Labor Statistics, the growing demand for Data science will open around 11.5 million jobs by the year 2026. But with the increasing demand, a huge scarcity of qualified Data scientists is also observed in this field. The rising demand for specialists in the Data Science field with maximum technical enhancements taking place in the world now. Thus, the professionals can utilize this best time to pursue a career in the area which will surely play a significant role in shaping their future.

This article is listing some of the career opportunities in the data science field; most of these job roles are combines in start-ups and mid-size organizations.

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What is Data science? Nowadays, Data science is utilized by most of the computing experts who have the knowledge of collecting, storing, handling, and examining data which is an essential resource for organizations to permit for data-driven decision making. Nearly, each communication with technology involves data, for example, Amazon, Facebook, Netflix, and face recognition system in which it is also required to sign in to your mobile. Amazon is a leading example in which data collection plays an important role and how it can be helpful for the regular customer. Amazon’s data sets can remember what has been purchased, how much amount is paid for a particular product, and which product has been searched more. With the help of this information, Amazon can customize its succeeding homepage views to fit your daily requirements.

For example, if you search for baby products, or groceries item, Amazon will not spam you with adds or product recommendations, but it may display the list of those items which may fit your requirements and benefits you. Similarly, Data Science can be helpful to reminding you of the usual purchasing. Let say if you purchase groceries each month, you may receive a notification about the discount coupon or deal related to products around the same time every month.  This data will act as a trigger which will remind you to purchase your product. Data Science is beneficial for both companies as well as customers.

With the help of Big Data, a retailer’s profit may increase up to 60 percent and services permitted by personal-location can enable customers to capture $600 billion in economic, which means they can purchase a good or service less than their expectation. Data Science can help the retailer to increase profitability concurrently and to save customers money.

Most Demanding Data Science careers: Not only in the technology field, but Data science professionals are required in almost every job division. Amazon, Google, Apple, Microsoft, Facebook are some of the biggest technology firms that hire a few percent of U.S. employees. But, to get a job in these high-paying, demanding job roles, a candidate should possess an advanced education. Well, most of the Data Scientists are highly educated, around 90% of them have at least a master’s degree, and 45% have a doctorate, and though there are some notable exemptions, a solid educational background is needed to develop the comprehensive skills essential to be a data scientist. So, let’s explore some of demanding Data Science careers.

1. Data Scientist: Well, this is one of the most demanding data science careers. Data Scientists are responsible for examining small and big data. They are also responsible for identifying, cleaning, and organizing big data for businesses. The Data scientist job includes the creation of data products such as recommender systems. These recommender systems are used in various platforms such as Amazon or Flipkart. Data Scientist practice advanced analytic technologies such as Machine Learning and predictive modeling which are examined through enormous amounts of structured, unstructured, and semi-structured data to identify definitive patterns. Based on the results of their investigation data scientist provide relevant understanding beyond statistical analysis to support an organization make planned business decisions and obtain a competitive advantage. The average salary of a Data Scientist is approximately $139,840. As compared to data analysis, Data scientists are much more technical. A Data scientist can get a job in prominent companies such as Facebook, Airbnb, and Twitter.

2. Business Intelligence (BI) Developer: Another career option in the Data Scientist field is a Business Intelligence developer. They are considered as one of the most wanted Data science professionals in the corporate world. Business Intelligence developers are responsible for designing and developing policies which help the organization in making better business decisions. In order to make the understanding of system procedures easier, a business intelligence developer can either utilize current BI analytic tools or build their own tool. Along with this, they are also responsible for regularly developing and improving IT solutions, by coding, designing, testing, debugging and implementations of such tools. The average salary of BI developers is approximately $89,333 per year. Business Intelligence developer can get a job in top companies such as DollarShave club, Liberty Mutual, etc.

3. Business Analyst: Business analyst is the next career option in Data Science field. They are the old graduates and responsible for examining data which are mainly based on the business decision makers. Business Analyst holds good information of the organization and the business domain of different industries such as telecom, retail, health, e-commerce, manufacturing including various functional areas like marketing, finance, HR, operations, etc.

Business analysts provide understandings based on data which helps to answer a few questions such as why some product failed. What will be the sales for the future quarter? etc.

4. Data Engineer: Data engineer is another career option in the Data Science field. They share a cooperative relationship with Data Scientist. Data engineers are responsible for making the data understandable and readable for the data scientist. They not only develop and maintain the analytics structure, and drive almost every function in the data domain, but they also create the data set processes used in modeling, mining, achievement, and verification. Data engineer manage the development, construction, maintenance, and testing of architectures such as databases and large-scale processing systems. Also, they can carry out real-time processing of the collected data. With the help of self-created or available data analytics systems, they can also work to improve the quality and quantity of data. A Data Engineer can get an average salary of $151,307 yearly. The top companies for Data engineers include Spotify, Verizon, General Motors, etc.

5. Data Analyst: Data analyst is one of the promising career options in Data Science field. A data analyst is answerable for tracking web analytics, analyzing A/B testing, operating and altering large data sets, to be line up with anticipated analysis for businesses. With the help of statistical techniques for data analysis, a data engineer produces meaningful business reports, and endorse new conducts to lower spending by enhancing the efficiency of business processes. They also must work jointly with the management to develop a priority-based list of corporate and data requirements for their projects. With the help of available data, they can create a model that displays the entire customer trends and the consumer population. The average salary of a data engineer is $83,878 per year.

6. Machine Learning Engineer: The primary task of a Machine Learning engineer is to create a data funnels and provide a software solution. Along with this, they are also responsible for exploring and applying suitable machine learning algorithms and tools. By learning and transforming Data Science prototypes, and picking appropriate datasets and representation techniques, they also design machine learning systems from the beginning. A machine learning engineer carries out a statistical study of the systems via frequent tests and experiments, and according to the test outcomes, they modify their operations. A machine learning engineer can get an average salary of $139,840. They can easily get an excellent job in the top companies such as Apple, Expedia, Tinder.

7. Enterprise architect: An enterprise architect is responsible for managing the creation, improvement, preservation, and management of IT architecture models, and IT support system, including their lower level components by working thoroughly with stakeholders, subject matter experts, and management. In order to generate an IT system architecture that supports organization, they also assess a company’s business planning prudently. The average salary of an Enterprise architect is $161,272. They can get a job in the top companies such as Cisco, Microsoft, Boeing, etc.

8. Data Architect: Well, Data architect is another career option in the Data Science field. They are answerable for the advancement of data solutions for multi-platform presentation and design analytics applications. They confirm agreement with company policies and external rules, maintaining the integrity and security of the company database by investigating database application approaches carefully. The average salary of a Data Architect is $137,630.

9. Application Architect: An application architect is responsible for following the behavior of an application utilized within a business and their interaction with each other and with other operators. The average salary of an application architect is $134,520. The top companies in which application architect can get a job is UPS, Dow Jones, Oracle, etc.

10. Infrastructure Architect: This is again a promising job role in the data science field. They are responsible for supervising all business systems whether they are functioning correctly or not and also support the development of innovative skills and system requirements. The average salary for an infrastructure architect is $126,353. The notable firms are Abbott Labs, Hewlett-Packard, Dell, etc. which has job options for the infrastructure architect.

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Conclusion: It is expected that few data science task will be automated in the upcoming years, but the data science professionals are always required who comprehend a business need and can develop and implement a data-oriented solution. Data Science professionals are needed in almost every field. Today, most of the businesses and government divisions depend on big data to become successful and support their customers. Data Science careers are in great demand and this trend will continue in the future as well.