Data science<\/em> features significantly when you talk about high paying future jobs.<\/strong> To understand what\u2019s driving this trend, which experts say will only grow bigger with time, you have to know how the technology landscape has experienced tremendous growth and undergone a sea change over the last few years, thanks to a high degree of reliability on computer programs, data-based analysis, and digital technology. Working with huge sets of data and making data-driven decisions, in particular, have become a norm in most sectors \u2013 from banks and fintech to transportation, IT, and more. After all, almost each interaction with technology has data at its core. Thus, be it your online purchase at marketplaces like Amazon, streaming recommendations for you on Netflix or Amazon Prime, your Facebook feed, or even the facial recognition necessary to sign into your phone, almost everything leverages data to perform certain operations. No wonder why data science is the flavor of the season. Experts in the domain of data science predict that in several companies and industries that are less tech-forward than others, where data science today plays a supportive (or back-office) role in the business, the scenario will change soon in the future as these companies and industries get ready to access a huge amount of data \u2013 both structured and unstructured, as well as feedback loops. And that\u2019s what makes the job of data<\/strong> scientists<\/strong> one of the most coveted ones in today\u2019s world.<\/p>\n

Before talking about the high paying future jobs <\/strong>that can take up, let\u2019s take a look into what makes the job of data scientists<\/strong> is so popular.<\/p>\n

1- What makes data scientist a popular job?<\/strong><\/h3>\n

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Over<\/em> the last couple of years, it has been noticed that not only are people taking up courses and enrolling in bootcamps to get job-ready but may are even contemplating a career change as being a data<\/strong> scientist <\/strong>would bring a fat pay packet and a lot of prestige their way.<\/p>\n

For 2019, data scientists<\/strong> are ranked as the no. 1 most promising job in the US according to a LinkedIn report. For this report, LinkedIn scrutinized data from millions of job openings, member profiles, and salaries, and used five factors for ranking the top roles. These factors were career advancement, salary, number of job openings in the US, their extensive regional availability, and year-over-year increase in the job openings.<\/p>\n

Even on Glassdoor’s list of Best Jobs in America, data scientist<\/strong> sat pretty at the top for the past three years, which matches what professionals say they like about this field – high salaries, high demand, and high job satisfaction.<\/p>\n

Those aiming for a high paying salary shouldn\u2019t think twice before taking up the job of a data scientist<\/strong> as these professionals enjoy a median base salary of $130,000, and noticed 56% more job openings this year than the previous one, according to the LinkedIn report. At present, there are over 4,000 data science job openings all over the US.<\/p>\n

2- A prediction of the nature of<\/strong> high paying future jobs in the domain of data science<\/strong><\/h3>\n

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Apart<\/em> from being data scientists, professionals in the field of data science can also get ready to take up high paying future jobs <\/strong>such as data architect, big data engineer, database manager, business intelligent analyst, etc. All these jobs pay salaries that are the best in the industry and help businesses make good choices by leveraging data science skills to analyze loads of data and drawing useful insights from them.<\/p>\n

Let\u2019s take a closer look into what each of the above-mentioned jobs would entail.<\/p>\n

2.1- Data Architect<\/strong><\/h4>\n

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A<\/em> data scientist<\/strong> working as a data expert would assist in maintaining and designing data and its structure that keeps changing while strategizing and developing efficiency models along with working on continuous design improvements with a strong emphasis on visualization.<\/p>\n

2.2- Big Data Engineer<\/strong><\/h4>\n

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This<\/em> professional is responsible for examining huge amounts of data and changing it to make it suitable for drawing valuable insights, which could be relied upon by business owners and top executives to improve their business operations. As a big data engineer, you will also analyze, acquire, report and digest on the incoming and outgoing data of the company. Additionally, you will provide oversight of software, hardware, and data infrastructure to ensure that the information is being effectively utilized and streamlined.<\/p>\n

2.3- Database Manager<\/strong><\/h4>\n

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This<\/em> professional is in charge of maintaining the databases of an organization along with handling issues that crop up by diagnosing and fixing them as quickly as possible. It\u2019s also the duty of database managers to manage the database and its hardware while ensuring the databases are up to date and compatible with newer software and systems.<\/p>\n

2.4- Business Intelligence Analyst<\/strong><\/h4>\n

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A<\/em> data scientist<\/strong> working as a business intelligent analyst would be responsible for evaluating data from the business and converting it into useful information that top company executives and those at managerial\/director levels can rely upon to improve their business operations by taking data-driven decisions. Your job responsibilities would also include crafting data reports that are independent and help to depict patterns, trends, and modernization processes that can enhance business operational protocols.<\/p>\n

2.5- Data Analytics Manager<\/strong><\/h4>\n

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Several<\/em> companies are increasingly relying on these professionals to make sense of the data and convey it to the rest of the team with an emphasis on understanding how businesses need to respond to the insights drawn from data. Data scientists<\/strong> with a rock-solid analytics and business background along with managerial skills are the ideal candidates for the job of a data analytics manager.<\/p>\n

To understand the true nature of high paying future jobs <\/strong>for data scientists<\/strong>, it\u2019s important to understand what the landscape of data science will look like in the future. Dan Wulin \u2013 Head of Data Science and Machine Learning at Wayfair, gives a glimpse into it, though he says he could be wrong with his predictions.<\/p>\n

Dan Wulin puts forward three broader trends, which he believes will shape the future of the data science landscape, and thus, even have an impact on the type of high paying future jobs <\/strong>that data scientists<\/strong> can take up.<\/p>\n

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1. More and more complex data science algorithms will keep on to be included in technologies and packages that make their deployment easier: To understand the implication better, you can simply compare the experience of training and deploying something such as a random forest in the present day to what it was about 10 years ago. Today, it is orders of magnitude quicker to apply, can be completed with less statistical and technical knowledge, albeit with a higher degree of quality. This has emerged as a common trend across numerous areas in data science and will continue to get bigger.<\/li>\n
2. Adoption of ML, AI, and related techniques: As an increasing number of companies looking to gain bigger and better insights from data they collect themselves and the ones amassed by their partners, a bigger emphasis will be on hiring data scientists<\/strong> and other data science professionals. At the same time, businesses will continue adopting machine learning (ML), artificial intelligence (AI), and related techniques in ways that have a positive impact on their businesses in fundamental ways.<\/li>\n
3. A shift in the nature of work data scientists<\/strong> are doing today by getting them transferred to workers trained in statistics and coding but are less highly-trained: To keep pace with the market demands and broader shifts in industries toward machine learning, AI, etc., academic programs will start exposing students more and more to statistics, engineering, machine learning, and linear algebra in their coursework. Thus, by encouraging and pushing their students to develop suitable technical skills, these academic programs will get students more job-ready than they are today. This, in turn, would mean much of the work done by present-day data scientists<\/strong> will ultimately be transferred to less highly trained people who have adequate statistics and coding exposure to use robust packages and technologies successfully and build machine learning models.<\/li>\n<\/ol>\n

   As a result of points 2 and 3 above, the roles of data scientists<\/strong> would ultimately evolve in the future. One path would lead to high paying future jobs, <\/strong>which will be somewhat similar to jobs handled by present-day data science teams that involve driving extremely research-oriented work that goes beyond normally available techniques. An instance could be the application of machine learning at deep levels, something similar to what various use cases such as image classification, autonomous driving, etc. are leveraging today.<\/p>\n

   The second path would lead to high paying future jobs<\/strong> that deal with fulfilling business-side management roles, for which companies today usually hire MBAs. As data science is poised for fast and extensive growth in the coming years, increasing value would be put on being able to link the domain\u2019s fundamental techniques to business problems in a meaningful way. Thus, a growing demand would emerge for technically trained individuals who possess solid communication skills and boast of business common sense. This means data scientists<\/strong> having these skills would be in the best position to capitalize on such new opportunities that open up in the future.<\/p>\n