Data science is the fastest-growing tech career predicted to keep expanding. More and more businesses use data to increase revenue and improve decision-making; simultaneously, user data is used for software personalization and ad campaigns. Healthcare and public transportation sectors use Big Data for unprecedented insights into widely complex industries.
It’s not only that institutions use data but also how they use it. Machine learning specialists help construct complex algorithms to streamline business processes and add additional value to the service. Software built on ML algorithms uses user data to better adhere to their needs, whether Netflix recommendations or illness diagnosis.
Although intertwined, machine learning specialists and data scientists are not the same. Here’s an overview of the current job market for these two professions.
Open Job Positions
The World Economic Forum ‘Future of Jobs Report’ 2023 is out and looks promising for data science. According to their statistics,
Demand for AI and Machine Learning Specialists is expected to grow by 40% or 1 million jobs, as the usage of AI and machine learning drives continued industry transformation.
The numbers could be even higher depending on how well innovations like ChatGPT adapt to our societies. However, it’s a safe assumption that AI-powered technologies will successfully improve people’s daily lives, demanding more specialists create new solutions.
There are eight paths a becoming ML specialist can choose from:
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1. Software engineer
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2. Software developer
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3. Machine learning engineer
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4. ML data scientist
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5. Business intelligence developer
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6. Natural language processing scientist
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7. Computational linguist
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8. Machine learning designer
Software developers or data scientists are great starting positions, with ML engineers moving more toward the tech side and business intelligence toward sales.
Machine learning and data science are not suited for IT beginners, although the latter has more junior positions. Most specialists come from a computer science or higher math education background. However, you can choose an elaborate online data science course if you switch careers. Advanced online learning platforms are optimized to provide the closest work-like experience possible and significantly increase the chance of landing a desired job opportunity.
Data Science is a broader concept. However, many data scientists use machine learning algorithms, often overlapping these two disciplines. Many companies in various industries seek data scientists. For example, they work in energy, agriculture, health care, banking, entertainment, software development, cybersecurity, etc. The list is almost endless, as most successful businesses nowadays use data.
Data scientists provide data-driven insights into business or software development. Usually, they start as data analysts who aggregate data, outlining performance issues. Data scientists then use this data to provide improvements in solutions. In other words, data analysts focus more on the past and scientists on the future. You can also branch out to data engineering, responsible for data gathering and storage systems if software development is more your thing.
In 2021 there were more than 113K open data scientists job positions in the US. It’s a worldwide tendency, as big data opens opportunities for many businesses. Data scientists can work in healthcare or code Netflix algorithms for personalized recommendations. The job market is diverse, and you can choose your preferred industry.
A Look Towards the Future
One of the most important things to ask before choosing a career is: how long will I work there? In our fast-paced century, jobs come and go, and artificial intelligence threatens to eliminate even more manual positions. Are ML developers and data scientists safe?
Currently, these are among the safest IT career choices. Although AI can replace many tasks, it lacks the critical thinking required for working with data. Machine learning algorithms aim to include new data in automated self-teaching processes automatically, but human oversight is mandatory at all steps. Time and time, AI has proven to generate misinformation or falsely interpret data to be left alone.
Data scientists can feel safe knowing that Google, Meta, and Amazon will keep fighting for user data in the upcoming few decades. These giants generate tens of thousands of job opportunities and set the standard for business development, with other companies replicating the business model. Data scientists bring exceptional value to all sectors by illustrating their weak points based on statistics and providing solutions with accurate success estimations.
Lastly, some IT professionals consider ChatGPT a threat that could automate data gathering, analysis, and coding tasks. ChatGPT will not replace senior professionals, just like the calculator didn’t make everyone a mathematician. Most likely, AI-powered tools will become an everyday assistants in our day-to-day life. Still, they will hardly eliminate the need for cognitive labor, not at currently predicted levels.
Conclusion
To summarize, getting into machine learning and data science is as good of a time as ever. Sharpen your brain cells and prepare for continuous and rewarding learning because these two disciplines will pave the way for many crucial upcoming tech innovations.