Data science career growth is one of the fastest worldwide, with Harvard Business Review calling it the hottest job of the 21st century and LinkedIn naming it the fastest-growing job in 2017. Business leaders call data the new oil.
It is predicted that there will be roughly 11.5 million new jobs in the field by 2026, and the Big Data market size will be an estimated USD 96 billion by then. Yet, despite all these numbers, there’s a wide gap between job postings and talent in the field. According to quanthub, the global tech shortage is expected to touch 85 million in the next ten years.
According to PwC, in the Middle East, Artificial Intelligence (AI) – a massive driver of the data science industry – will be worth USD 320 billion by 2030 in the UAE alone. Thus, the region moves towards colossal development but needs an army of professionals and experts to bring it to its envisioned heights.
For professionals looking to change their profession or start one, the data science career path is the place to be.
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Data-Driven Career Paths
Here are the roles for a data science professional to choose from.
Data scientists overlook projects from start to finish. They have a complete understanding of the business problem and analyze and organize information that solves the problem. They are the best professionals to share holistic insights, discover patterns, share solutions, and predict future trends concerning the problem. Generally, in large organizations, data scientist skills are seen in action leading the project instead of delving fully into execution-level details.
As the title suggests, data analysts are the ones who dive deep into information – structured or unstructured – and analyze it. They perform search queries on a database and extract valuable data for the business problem. They use algorithms and models to process, optimize and manipulate the data. A data analytics career path also involves visualization, meaning that they need to present the data through simplified charts and numbers.
Data engineers are the ones who design, build and maintain data ecosystems that data scientists use to run their algorithms. They also test these systems and pipelines to ensure highly optimized runs. Updating the data system is also the data engineer’s responsibility. They format data batches and match these formats to those in the data system, making the work of data scientist easier.
One of the newest and most creative data science opportunities, data storytelling, includes visualizing data, creating reports and statistics, and expressing these in a way that fits the narrative of the business problem. The data gathered by data scientists and analysts are often in complex, numerical, and statistical formats. Data storytellers bridge the gap between technical data and human understanding by crafting a story to simplify insights.
Machine Learning Scientist
A Machine Learning (ML) scientist is responsible for researching and developing new methods, algorithms, and approaches to data science. ML scientist is still an upcoming job role in this industry. ML scientists are generally a part of the Research and Development (R&D) division in any organization. They are in charge of finding innovative data processing and analyzing approaches, often leading to published work.
Business analysts have somewhat different functions than other data science roles. They are more in tune with the business aspect of the problem. Their responsibility is to use the data and learnings gathered to develop actionable insights for solving the business problem.
They have an overall understanding of data systems, handling large data sets, and organizing valuable data. However, the ultimate responsibility of linking data to problem-solving lies with business analysts making it one of the most fulfilling data scientist career paths.
At times, the professionals designing a database and those using it are different. In such cases, teams must be aligned so that data processing can continue efficiently. This responsibility lies with a database administrator. Database administrators monitor the database system and ensure its smooth functioning. They also keep records of data flow by creating backups. If an employee needs access to the database, they’re the ones in charge of granting permission.
Sometimes, organizations need experts of a particular function to get accurate results. And statisticians are experts who build a career in data science by using statistical theories and models. Statisticians are responsible for collecting, organizing, presenting, and analyzing data using statistical methods. They typically work in industries that need statistics for continuous functioning, such as sports, finance, transportation, market research, etc. They may also be academic experts.
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The field of data science is constantly developing. As such, the careers available in the industry aren’t limited to the ones mentioned above. Several specific roles are expected to emerge – Artificial Intelligence (AI) engineers, AI developers, Deep Learning specialists, ML system developers, and more.
Journey of a Data Science Professional
If you’re still wondering is data science a good career, you’ll find that data scientists see an exciting progression as they move up the ladder.
Usually, the professional is an intern, junior, or associate at this stage. Being an entry-level job, professionals are raw and work straightforward tasks. These tasks include debugging existing models.
Juniors or associates aren’t expected to build new models but run queries on current databases and statistical models to gather and analyze data. They’re generally the ones who execute and aren’t, necessarily, fully aware of the business problem. They’re assigned tasks rather than taking up jobs on their own.
After about two to five years, a junior data science professional is promoted to the ‘Senior’ job role. ML engineers, AI developers, Data Science Managers, Data Architects generally start in this position as the field needs more in-depth knowledge.
As a senior, data science professionals are architects of new models and products. They are aware of business problems and are in charge of running individual teams for a specific problem. They design new systems, remove logical flaws in current models, write innovative but reusable codes and build secure data pipelines.
At the most advanced level are the Lead data science professionals and Directors who oversee large projects, often mapping the solution path of the business problem and providing the layout for various jobs. They typically have a business mindset, understand varied business challenges, discover new opportunities, and are leaders.
They are equipped to deal with multiple organizations and projects at a time. They have combined, if not in-depth, knowledge of all database systems, ML and AI practices, and programming languages. It is the ultimate data science career objective.
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Enter the ever-growing field of Data Science
If you’re still here, chances are you’re interested in taking a step forward to become a data science professional. But you’re worried about how to start a career in data science with no experience.
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In the next few years, the field will grow exponentially. Join a transformative career path that will guide the way businesses are run and inspire the world to be a better place. The time to enter the world’s fastest-growing field is now.