Many organizations are embracing data science in decision-making. One of the issues they face is finding qualified professionals to handle the tasks involved. In addition, they need data scientists qualified to handle the development and running of initiatives associated with data science.
Organizations in different industries appreciate the benefits associated with data science. Still, the shortage affects them, and they cannot fully take advantage of everything a data scientist can help them achieve.
Netflix is a good example. This is based on algorithms in machine learning. Organizations like Google and Amazon are also enjoying data science and driving engagement more than ever. Organizations can benefit from data science. With augmented intelligence and machine learning, they can:
- Develop some predictive models
- Identify fraud
- Predict supply chains’
- Develop recommendations
- Profile potential customers
- Identify potential customers
Data scientist jobs are in high demand, and their reward is also great. Job services post many data scientist opportunities.
Increase in demand
Companies need data scientists, and there is a great emphasis on this. There is still a shortage of qualified individuals. As a result, job postings still need to be answered. Many companies link the lack of data scientists to their inability to compete with a business that trains staff themselves even when they don’t have university degrees.
The high demand requires the existing programs in data science to be expanded. It is only in this way that the deficit can be met. In the past, a Ph.D or a master’s in a related field was needed before you could be allowed to take up a course in data science. As a result, undergraduates couldn’t leave their learning institutions and get a job in data science.
The strict academic policy left a vast gap, and it is estimated that even more jobs will be available worldwide, but there may be someone to fill them. Even with the institutions offering courses, there are very few students, and they cannot meet the demand in the market. The remuneration is very good for anyone taking up a career in data science. However, for one to thrive in data science, one should bear the skills and be talented enough to keep up and handle the challenges that keep coming up constantly. Data science is a dynamic field, and things keep changing occasionally.
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Skills needed in data science
Data science covers many things. This is why many people shy away from the field. It is also rather dynamic. Therefore, you must keep up learning even in employment to remain relevant and current with the latest trends, techniques, and tools.
Interested parties should have machine learning, statistics, and database language skills. They should be interested in programming languages. While you don’t need coding to start in data science, it is a skill that can help you rise higher in your role and job description.
To pursue data science, you must be interested in software engineering and data mining. Some additional skills that can help you include confidence in communicating, analytics, problem-solving, and business acumen. Intellectual curiosity regarding a subject is needed as well. With the technical skills, there are other areas that a student would need to perfect to ensure that they excel in class and work settings.
Main factors causing the shortage of data scientists
The tech industry has been a bit unstable. When the pandemic hit, many people were hired in tech, but resignations followed. Afterwards, there were many layoffs. The layoffs affected many people, and even the biggest tech companies like Meta and Amazon were not spared. A lot of data scientists were laid off during this time.
Demand for qualified data scientists is still growing. As a result, many roles needing data science expertise will also grow, even with many companies struggling to find qualified personnel.
These studies leave us wondering what exactly is causing the shortage. While people have different opinions, some factors could be causing this.
Downgrading of data scientist job titles
Some companies are trying hard to downgrade job titles as they try to fight off the economic uncertainty evident in many parts of the world today. In a recent analysis, between October 2021 -October 2022, the number of openings in data scientist jobs dropped by 26 percent. There were more data analyst and data engineer job openings in that same timeline. These sentiments are also echoed in anonymous communities and forums where employees discuss issues. Many people claim that data science areas are being downgraded to mere analysts. The roles of data scientists are now being split into various names, such as machine learning engineers and data analysts.
This is not a good thing for those experienced and trained as data scientists. The only people who stand to benefit are the employers, with most trying to save money. A data analyst does not earn as much as a data scientist.
This analysis makes the following observations:
- The roles of data scientists are diminishing, but there is a rise in the demand for AI researchers, software engineers, and ML engineers.
- Most firms are separating data engineer and data analyst roles from the roles of data scientists.
- Companies are making job listings for data scientist jobs, but no interviews or actual resume reviews exist.
- Most firms consider data scientist’s roles a bit expensive, mainly due to the recession that has caused cost-cutting and layoffs.
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Do data scientists add any value to businesses?
Data science is a complex field to understand. One thing to note is that having academic prowess does not always mean great performance. Data scientists can add value to business, but they need many skills that can take years to master. To add value to a business, a data scientist needs to:
- Know math, statistics, probability, and programming. In addition, they need presentation skills, software development, computer science, and business domain knowledge.
- Have the capacity to acquire, process, clean, integrate, and store data
- Be able to explore and investigate data and pick the best models and algorithms
- Be capable of reiterating and delivering value constantly
For companies willing to hire data scientists, finding someone with all the skillets is difficult, making it more demanding.
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