5 Data Science & Machine Learning Career Trends for 2019

April 10, 2019 | Digital Skills

At this point, most of us tech people know we need some data science & machine learning skills in order to survive and thrive. But with so much buzz around these technologies, it is easy to lose track of what’s really important.

I see a lot of people running after the hype, trying to acquire lots of me-too skills without due diligence. As a result, the mismatch between acquired and required skills continues to grow.

Amid this overwhelming mess, knowing what trends are currently shaping the data science and machine learning landscape can help you be prudent about identifying the right skills where you need to invest your time and efforts.

So, let’s see 5 such key data science and machine learning career trends for 2019 that will help you build a future-proof career.

1. Companies Want Specialization

‘Data Scientist’ and ‘Machine Learning Engineer’ are fascinating job titles, but they are incomplete. Today, the industry has matured and companies are looking for specializations within these fields. For instance, here is a glimpse of all the data roles at Netflix.

Besides, most AI startups operate in niches; therefore, require skills specific to that niche. For example, if a company is building an NLP solution, instead of posting a job vacancy for ‘machine learning engineers’ it would look for ‘NLP engineers’. In fact, if you look it up on LinkedIn, you will see every machine learning or data science job vacancy with some sort of specialization with it.

2. The Demand of Data Engineers Rises

There is an oft-cited LinkedIn survey that states that in 2018, the demand for data engineers exceeds the demand for data scientists. Data engineers are responsible for developing software components of analytics applications. They collect and store data and do real-time processing to ensure uninterrupted data flow so that data scientists can analyze it seamlessly.

For the past couple of years, companies have been hiring data scientists relentlessly. As a result, now they don’t have enough resources to provide their data scientists with the required infrastructure, which automatically makes data engineering one of the most prominent digital skills to have in 2019.
Apart from being proficient in programming, data engineers also need to be proficient in Hadoop, MapReduce, Hive, MySQL, Cassandra, MongoDB, NoSQL, SQL, & Data streaming and programming.

3. Industry Drastically Lacks AIOps Engineers

Quick definition: AIOps for data science/machine learning solutions is what DevOps is for traditional software development.

The abundance of data scientists (only in comparison, the industry still needs a lot of data scientists) has not only increased the demand of data engineers, but it has also triggered the demand of engineers at the deployment end (AIOPs). The rough chart below sheds more light on it.

If we consider the current state of AI in the corporate world, the industry has enough resources focused on training models. But every model needs regular data and model versioning at the deployment end to ensure that the model continues to meet a business’s dynamic demand. And there, the industry faces a drastic lack of resources currently. So, all in all, this area currently presents heaps of opportunities. And the technology stack you need to learn for that includes frameworks such as TensorFlow Serving, Docker, Kubernetes (K8s), and Kubeflow.

4. Python Is the Present & Future

On the internet, there are already tons of resources on Python Vs R Vs SAS. But when it comes to machine learning and data science, it is already established (although arguably) that Python is the way to go, because it has the packages specifically designed for these jobs.

For beginners the trouble is that lots of tutorials and courses on the internet are based on R. For instance, on e-learning platform Data Camp, roughly 2/3rd of data science and machine learning tutorials are based on R, only 1/3rd in Python. But if you look at their respective communities, the Python community exceeds the R community by a great margin.

Now, I am not recommending that you don’t learn R at all. It is useful for a number of purposes. But if you are aiming to build a career in machine learning & data science, you should rather spend more time on mastering Python. Besides, most of the deep learning frameworks you will use such as TensorFlow, PyTorch, and fast.ai are all based in Python.

5. A Portfolio Is a Must

Now, being someone who is trying to enter the data science and machine learning world, this part can be a little tricky. These are new technologies, so, there is a slight chance that you have experience working on related projects. And employers are also aware of it. But that shouldn’t stop you from building a portfolio.

Online portals like Github and Kaggle offer you the platform to showcase your work on whatever individual projects you are pursuing – as a part of developing a new skill. Pretty much, every employer would ask you for Github and Kaggle profiles in the present scenario. So, be ready with them, instead of excuses.

Concluding Remarks

The rapid growth of the digital landscape will continue to require professionals to constantly update their digital skills. For tech-savvy professionals, it means loads of new opportunities, and new horizons of possibilities what they can do with the technology. But in order to take advantage of this dynamism, professionals need to stay abreast with the on-going state of the digital landscape all the times.