Good news. Data scientists are still in demand going into the new year. However, for many of you, the chance to break into data science could come with a slight pivot. Let’s take a look at the data science market to see how things are shaping up for this coming year.
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Scope Out Data Engineering
Last year, data science needs were on the rise. As companies begin to learn the aspects of data science outside of the umbrella term “data science,” we’re hoping to see more targeted job descriptions free of the incomprehensible requirements we’ve seen in the past. HR is catching up with data science, and C-suite is ready to start implementing logical data science.
We see that data science doesn’t work without a logical framework. It’s too much to ask data scientists to both create the models to process data and handle framework maintenance as well. We’re seeing more efforts to separate those two fields into distinct job descriptions.
On Indeed, there are nearly 133,000 job postings for data engineering. “Data scientist” has just over 12,000. While we could attribute some of that to misclassification, it’s looking like more and more companies are understanding that they may need a team of engineers to pave and smooth the way for the work of a data scientist or team.
We’ve come to terms with the reality of big data, but as we move towards fast data, the process of handling swift data to scale will involve the work of both engineers to manage the pipeline and scientists to build the models.
This iterative process is impossible to handle by just one person in cases where big data, legacy systems, and other factors like streaming are involved. Organizations are shifting their data science teams to handle big data legacy systems and build pipelines for continuous intelligence.
Smaller organizations are getting on board with predictive analytics and its potential to drive insights in real-time. Companies are taking advantage of this for hyper-personalization and the potential for IoT devices to create a solid feedback loop.
Security is Still Champion
Despite all the hype cycles, people are still nervous about their data and about the artificial intelligence systems cropping up. Security remains even more critical. Organizations are moving towards cloud operations, but finding out that security there isn’t always there. The rise of edge computing for steaming and IoT security could be big career trends to watch.
The use of AI to combat AI-driven cyber attacks are also still a promising career option. Building machine and deep learning models that more accurately predict security threats could be a huge part of FinTech, IoT, really any field making use of data (hint, all of them).
The August Gartner Hype Cycle saw emerging technologies within five areas of development, all of which will need support from data engineers to build out frameworks. Whether you work on the framework side or you’re building the models, you’ve got a lot of opportunities out there. Gartner classifies it’s August hype cycle into five categories.
- Sensing and mobility – We’re invested in helping our robots see and move. Autonomous robots have applications within manufacturing, delivery and transportation, security, and all things IoT.
- Augmented Human – Along with the trend of augmented intelligence, amping up what humans are capable of doing with our AI tools is a huge trend happening as we speak. Better prosthetics, biotech, immersive workplaces—anything that involves thinking, feeling, or behaving like humans is big business. The most promising area, of course, is in health and medical.
- Postclassical compute and comms – 5G is here, and so is entirely new architectures. Computing on the nanoscale means bigger, better satellite communications, farther-reaching wireless coverage, and nano-technology.
- Digital Ecosystems – Digital ops is huge. Working on the next generation of digital ecosystem enables seamless connections between enterprises, people, and things. Potential within the decentralized web is the biggest application of advancing digital ecosystems.
- Autonomous AI – Advanced AI and analytics is still way up on the hype cycle. New algorithms within data science, new models, better and faster autonomous processing—it’s all there. Think of some of the biggest things happening in data science and AI: edge AI, explainable AI, adaptive machine learning, generative adversarial networks. There’s a place both for data scientists building the models and the data engineers, enabling the environment for innovation.
The 2020 Job Curve to Work in Data Science
Building a career in data science is still one of the most exciting and promising careers going into the new year, but you may have more freedom to pivot to specific disciplines within the field. If you’re more into building the frameworks and environments, troubleshooting, and tweaking the perfect pipelines and architectures, data engineering is a hot hiring field.
If you’re still aiming to get your foot into pure data science, there are so many areas coming up to work on both innovation and technologies that help ease organizations into a deeper relationship with their data.
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If anything, organizations are developing a more intense, more strategic relationship with data as it becomes data in motion, and one thing is very clear. Data science is going to be the biggest career field for a long while. You just have to know where to niche.
Want to learn more about the data science market and how to get a job in data science in 2020? Attend the Career Expo at ODSC East 2020 this April 13-17 in Boston and learn more in-person!