Data Science Hiring Trends 2020
With a population of 7.7 billion, our world generates 2.5 quintillion bytes of data every day. Consequently, data scientists, data engineers, and business analysts are among the most sought-after positions. Data Science is everywhere and predicted to grow over the next decade. However, its landscape changes over time with new introductions each day. Let’s learn about the latest Data Science Hiring Trends.
According to Search Business Analytics, the demand for data scientists is booming and will only grow.
The increased importance of data science has opened a number of career opportunities. Though an imbalance exists between the demand and supply of data science professionals, it results in a handsome amount of salary for those who have sufficient but efficient understanding of data and its technologies. Nevertheless, with an expectation to bridge this gap, we have compiled the most appropriate Data Science Hiring Trends in 2020.
Let’s have a look at what industry leaders have to say about the Data Science trends to watch out for in the year 2020.
1. Ramasubramanian Sundararajan, Head of AI Lab, Cartesian Consulting
We have been subjected to so much hype about data science and AI in the past few years that, by now, most organizations take it as a given that it has to be part of their business. However, the next few years will tell the story of whether they are able to adopt data science solutions effectively and use them to generate business value. This is likely to result in 3 major trends:
(i) While data science was viewed as a separate discipline until now, it will start being viewed as a necessary component in management education in general. If you’re going to be a decision-maker in the 2020s, the expectation will be that you’ll be a data-driven decision-maker, a data science-aware decision-maker. You might be in finance or marketing or an HR major, but increasingly, working knowledge of basic data-driven methods will become table stakes if you want to get a good job.
(ii) Much of data science adoption in everyday business processes will depend on how deeply embedded it is. The less friction there is in adopting something new, the more likely it is to be adopted. Therefore, skill sets that will become crucial will be things like system integration, data engineering, and UI/UX design. These may have been viewed as support functions to a data scientist in the past, but they will have a crucial role to play in driving adoption.
(iii) We are likely to see increased adoption of tools that automate a lot of processes, including those in data science. Products/platforms that facilitate/automate the building of data science models will become the norm rather than the exception. However, these aren’t magic wands that you can simply wave in front of your business problem. You’ll need some degree of skill to identify the right problem and know what to tinker with to get the most mileage out of your preferred tool. This skill – the ability to not just use automation tools but also transcend them – will become increasingly valuable in the coming years.
2. Madhu Vadlamani, Sr. Lead – Web & Data Analytics, Kony, Inc.
Data scientist – The most searchable word in Google and the sexiest job of 2020 + years. The job numbers are increasing on a day-day basis at every firm, irrespective of the industry. A simple search in any of the job portals would give a better answer on how the trends are. The demand is huge, starting from top firm to the startups which are running for a unicorn – everyone needs data scientists.
Handling the huge data is not just a task but it’s a responsibility. It’s never a cakewalk when we handle huge sets of data. Where can I get the data, what to do with this data, what can I automate, when can I predict? As I said above the job is never a cakewalk. This needs experts or expertise on the segment in that one handles.
What does the industry expect from data scientists? Imagine how scientists behave. Their target is to solve a problem. Before that – they do find what is the problem and then think about the causes of the problems. Then they collect data, filter the data which is needed and after filtering they draw lines of statements on what to do, how to do, when to do and where to do. In other terms, scientists are the real data scientists who handle a lot of data for their experiments/results.
Now, in a corporate world, technology is available but how to use the technology needs experts. One needs to collect the data – connect the data and draw the conclusions. They look for someone –
1) Who is – good at machine learning algorithms
2) Who is – good at python
3) Who is – good at visualizing the data – like tableau
Apart from the above, every industry needs someone who can think and work but not just work. They should able to visualize the outcomes and the power of imagination makes the perfect data scientist – who can able to see reel vs real, generic vs genuine, false vs facts
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3. Shivaram K R, Co-Founder & CEO, Curl Analytics
It is rightly said, data is oil and AI is the engine. The data generation is increasing exponentially. It is estimated 2.5 quintillion bytes of data are generated by all of us every day. All this data needs to be analyzed and made useful. This needs a large number of AI experts.
At Curl, we interact with many enterprises, small and big. We work with organizations across the globe. What we see is Machine Learning (ML) and Artificial Intelligence (AI) adoption is accelerating at a breakneck pace. We see there is a great demand for hiring ML experts. We at Curl analytics are trying to hire many ML experts in the areas of Image analytics, NLP and general-purpose ML. We interview many candidates and hire a select few. We feel there is a huge death of talent in the industry today. Knowing how to build an ML model is one thing, understanding how it works, knowing how to tweak it to get the best results and how to optimize is something one learns by building many models. We see a few data scientists with this kind of understanding of ML.
Globally, I feel there will be a great demand for data scientists with expertise in image analytics, text analytics, and numeric data analysis. Image analytics and Computer vision are needed in many tasks such as autonomous cars, visual inspection, document processing, entertainment industry, etc. Text analytics and NLP are needed in all the enterprises for processing huge corpus of text data they have. BFSI, health care, manufacturing, you name it needs NLP experts. The conventional numeric data needs ML experts who have worked on a large number of features and different data sets. Again, almost all industries have such data.
It is said, 5 out of 6 enterprises are not doing justice to the data they have. The ones who are doing justice are in top positions today. The trends in data science will continue easily for the next 5 years.
4. Ajay Ohri, Data Scientist, Sapient
Full-stack Data Scientists:
Data scientists should be ‘full-stack’ in order to be hired. This means they should know machine learning, deep learning, coding in R/Python/SAS/Scala, Big Data projects (PySpark), as well as productionalization of models of machine learning. They should know cloud (AWS / Azure/ GCP) as well as be exposed to Docker and Kubernetes
Freshers need to have skills and projects. For non-experienced data scientists to be considered for interviews, they should have skills – tools like R / Python /PySpark techniques – Machine and Deep Learning as well as projects – in internships, educational or private education, internships, kaggle as well as Github. In addition, they should participate in Stack Overflow not just read from it.
Data scientists need to be consulting client-ready. It is not enough for a data scientist to know tools, techniques, and applications. They should know how to solutionalize a particular business problem, approach a client with a listening consulting attitude and build use cases for further projects
Data Science hiring trends are followed by numerous opportunities across the world and all the credit goes to the increasing popularity of technology and data-oriented decision making. Companies are looking forward to hiring professionals who can understand as well as analyze data.
Since now you are aware of the Data Science Hiring Trends, it is time to make the most out of it and elevate your career as a Data Scientist.
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