Data science job opportunities continue to grow in 2022

data science job opportunities in 2022

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Pessimism can thrive in a pandemic, inducing tales of dystopia and an impending apocalypse. But those with a grip on reality will prepare themselves for the future with a positive outlook. They may be contemplating somewhere different to live, healthier lifestyles and new careers. 

The times are promising for those interested in greater employment prospects and an interest in data analysis and science. The World Economic Forum estimates this year will see data scientists and analysts ranked the number one role worldwide. By 2026, the US Bureau of Labor Statistics estimates data science will create around 11.5 million job openings.

This forecast should not come as too much of a surprise. Five of the world’s biggest technology companies contribute to more than 50 percent of the world’s market capitalization and are the biggest employers of data scientists and engineers. And technology is only one of many sectors that hires data scientists.

Organizations want these specialists for data-driven decision-making. Think about it: almost every purchase you make includes data – your internet and bricks and mortar shop purchases, Netflix choices, and fingerprint or facial recognition ID on your smartphone. Instead of being simply part of that data, you are now analyzing or engineering it for an employer.

Data science roles and what they entail

Common job titles in data science include data scientist, machine learning engineer, machine learning specialist, applications architect, data architect, data engineer, business intelligence developer.… The job titles go on. For example, one recent search query on listed more than 11,000 openings for “data scientist jobs”.

The analysis, collection, control and manipulation of data are at the core of all data science jobs. But their roles do vary considerably. 

Here are ten jobs that are promising for candidates with data science qualifications. Candidates can earn more depending on experience, and supply chain problems and inflation arising from government spending from the pandemic look sure to lead to pay rises, if only to keep hold of skilled employees in short supply.

  1. Data scientist (US$97,000) – typically charged with overseeing a firm’s data requirements. Analyze large amounts of raw and processed information to find patterns that will prove beneficial and influence future business decisions.
  2. Machine learning engineer (US$112,000) – creates data funnels and meets company needs with software solutions. Statistics, programming and software engineering knowledge will be put to the test. Design and build machine learning systems, and run tests and experiments to monitor their performance.
  3. Applications or software architect (US$160,000) – designs the architecture and building components such as user interface and infrastructure. The behavior of applications and how they interact within a business can also be in your remit.
  4. Enterprise architect (US$148,000) – aligns strategy with technology to meet business objectives. Must understand the business and its technology needs to design the requisite systems architecture.
  5. Data architect (US$123,000) – builds data solutions for performance and design analytics applications for multiple platforms. Improves the performance and functionality of existing systems and creates access for database administrators and analysts.
  6. Infrastructure architect (US$137,000) – oversees business systems for optimal working and supports new technology and system requirements. Typically oversees a company’s cloud computing strategy.
  7. Data engineer (US$115,000) – performs batch processing on gathered and stored data. Responsible for building and maintaining data pipelines for a robust and interconnected data ecosystem.
  8. Data visualization engineer (US$99,000) – monitors and optimizes the performance of the systems and virtual applications. Responsible for the support of the virtual environment. Diagnoses systems failures and takes corrective actions.
  9. Business intelligence developer (US$100,000) – designs and develops strategies for executives to find information to make better business decisions. Knowledge of business intelligence tools to build custom applications.
  10. Data analyst (US$68,000) – transforms and manipulates large data sets for analysis. This can include tracking web analytics, analyzing A/B testing, and preparing reports that communicate trends and insights.

Qualifications needed to work in data science

Science and maths qualifications are a foundation to build an education in data science. Computer science, engineering and physics degrees are also good preparation.

You can brush up on or add to your maths knowledge with a course to help you gain insights from statistics or devise a plan of attack after crunching numbers. But this isn’t to say that you have to be a computer or maths wizard. You might take some comfort from data scientists coming from various backgrounds. 

Importantly, good knowledge of your area of employment, such as accountancy, will be a good springboard into data science. Data science training coupled with your domain expertise is a powerful tonic for employers’ challenges to stay competitive. You will need to empathize with a business and understand its needs to devise a data-orientated solution.

Knowledge of programming languages, particularly Python, is essential. Companies look for Python, R, Apache Spark, Tableau and SQL. In some domains, to be towards the front of the queue of applicants, deep learning expertise in computer vision, NLP and mastery of Tensorflow and Pytorch is a must.

Software engineering skills are also crucial. Data structures and algorithms are increasingly becoming part of the technical tests for interviews, because more positions require writing production code and shipping your own models to production centers. Experience in Git, Docker and CI/CD knowledge is also imperative, depending on which road of data science you travel down.

What qualities do I need to succeed in data science?

Education is one thing; aptitude is another. That said, good communication skills will help drive your career in data science. 

Like other occupations, teamwork is evident in data science, and being a clear and concise communicator will make you an invaluable member of a data science team.

Business savvy will serve you well. Once you can identify consumer needs and create solutions from your data science skillset, you will have a promising career. If you have inexhaustible energy and are persistent in reaching a goal, those qualities will complement your data science education and training to no end.

A logical mind and passion for what you do will set you in good stead, too. These will be the engine that drives you.

Will automation replace data scientists?

Some data science work will be automated within the next ten years, and that’s progress. But to say automation will spell the end of the profession can be voiced only by the most negative of doomsayers. 

If anything, opportunities in data science will grow further. Data science will transform, augment, and move in other directions while requiring more input, new thinking, and an altered mindset to perceive what is possible and probable.

One data scientist in marketing talked about tools such as DataRobot and AutoML having the ability to build predictive models. Naturally, the reaction was a fear that data scientists were on the way out, like the alchemist, abacus and compact discs. 

But after using them, he found they created more – but different – work. For example, greater pre-processing of data was required, and it had to be formatted for users’ needs. The conclusion was the “heavy lifting” still had to be done by the data scientist, with the calculation that 80 percent of the tasks data scientists do cannot be automated. 

So long as you have domain knowledge, analytical and programming skills, and keep up with trends, you are unlikely to be replaced by an automated tool. Such tools might expedite workflow and require fewer resources, but they complement the work you do, not replace it.

Next steps

Skills and experience are acquired over time. To enhance your marketability or begin your journey in data science, you can pursue an advanced program in your area of interest. The Institute of Data’s full-time and part-time programs will help you achieve your career goals and connect you with thousands of industry partners seeking professionals with skills in data science. Talk to a career consultant now.

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