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Why Data Scientists losing their Jobs?

Last updated on March 1, 2021, 3:24 p.m. 5432 Views

Kirandeep Kaur

Kirandeep Kaur |

3+ Technical Content Writer. and who is passionate about his career

Why Data Scientists losing their Jobs?

Last updated on March 1, 2021, 3:24 p.m. 5432 Views

Kirandeep Kaur

Why Data Scientists losing their Jobs

The ongoing COVID-19 pandemic is adversely impacting the economy globally. Employees from all sectors are losing jobs and business disruption is already at the peak. The job roles associated with Data Science are not immune to the global recession and economic woes. Data Science is an emerging field in the 21st century and the professionals who work in this field are known as data scientists. You might have read many times on the internet about the high-paying job of a data scientist but do you know high paychecks don’t mean that a data scientist is sitting hand in hand and spending much! Being a data scientist means geeking out to solve complex problems and much more.

Every job title has its positives and negatives! With this article, I’ll be playing the role of devil’s advocate and expose a few of the facts related to the job of data scientists that are causing data scientists to leave their jobs.

Getting a data science job is not a piece of cake

In this era of digitization, data science has emerged as the core business disruptors and it’s not surprising that every business wants to be part of it. But remember, taking into this field is not a piece of cake. You need hardcore efforts, knowledge, and preparation of the data science skillset. You need to have a clear vision of what you want the analytics to determine. You should be well prepared and answerable to the employer from where you’ll get data. Data Scientists spend most of their time in locating, cleaning, and compiling data instead of searching for the values in the data and composing machine learning algorithms. It’s a fire-way to make sure that you, being a data scientist will quickly become bored and start looking elsewhere. Also, it is important for the data scientists to stay updated and keep up with the technology trends to ensure you actually know which software tool or hardware to utilize that could actually run algorithms.

Another point to be considered here is that many companies hire junior data scientists before they hire experienced ones. This might be because of the cost-saving of the company. But there should be a first person in the company who sets the analytics strategy and manage the data science practices. Analytics roadmap should be clear and the focus should be on data governance, documentation, along with practicing and managing data science initiative and the relationship between the two. So, being a data scientist is not as easy as playing video games!

Data Scientist Job expectation vs. reality

When we talk about the job of a Data Scientist, mismatched expectations are the most common reason why data scientists are losing their jobs. Playing with the algorithms is the first choice of data scientists in their jobs and that makes sense because complex algorithms help to solve business-related problems and influence business direction. To have a clear vision for the data science team is required to offer exposure to cutting-edge technologies. Data Science teams work with different departments to keep things working and it keeps things interesting. In this manner, they can view firsthand how their efforts are influencing the growth of the business.

Every company is so different and so are their work and responsibilities. Some startup companies hire data scientists without proper infrastructure. Not to be blamed for any organization as maybe they are unaware of the tools required of data scientists. Such basic reasons sometimes create big trouble for data scientists. This contributes to the cold start problem in AI. 

The data scientist likely came in to write savvy machine learning algorithms to drive understanding yet can't do this in light of the fact that their first employment is to sift through the data infrastructure as well as make analytic reports. Conversely, the organization just needed a graph that they could introduce in their board meeting every day. The organization at that point gets disappointed because they don't see esteem being driven rapidly enough and the entirety of this prompts the data scientist to be unhappy in their job. This puts lights in the two-way relationship between the employer and the data scientist.

Another reason that data scientists are illusioned is because of academia. If the company’s core business is not related to computer science but the data scientist degree is in the Computer Science field (as most of the data scientists are from a Computer Science background) then it could be a challenge for a data scientist. For instance, a person with commerce infrastructure knows nothing about software tools and technologies. In such circumstances, a data scientist could provide small incremental gains but not the bigger ones! Therefore, it is advised that freshers or beginner data scientists should constantly talk to their seniors and organization alumni to bridge the gap between job expectations vs. reality.

You’re the go-to person for anything related to data science

Expectations are never the same as reality. A data scientist is technically sound and is assumed to have an understanding of everything related to data. You are assumed to know Spark, Hadoop, Hive, Pig, SQL, Neo4J, MySQL, Python, R, Scala, Tensorflow, A/B Testing, NLP, anything machine learning. So for your colleagues, because you know everything, therefore, it is obvious that you have access to all of the data. So, every person is going to reach you for anything related to data. If business analysis departments need some suggestions for the sales funnel then they will reach data scientists as their first go-to person. As a salesperson, a data scientist is expected to answer the sales team to the questions related to analytics from different perspectives. Be it understanding the business-related problem, project execution strategy, or envisaging the larger picture in the business context, a data scientist is the first person to be outreached. What a data scientist is supposed to do?

  • Set up a strong correspondence between data science and business groups. They should be firm and co-ordinated.
  • Outfit business instinct and information from business pioneers. This can do something amazing for data analysts
  • Co-build up a quantifiable execution framework for the business to gauge the presentation progress of data analysts
  • Deftness assumes an extraordinary job in removing the best from a data scientist

As mentioned above, the tasks of data scientists are around “ABCD”. 

  1. Analytics (Data Analytics or BIG data is not only about Statistics, Tools, Techniques and, Visualization)
  2. Business (Working on business outcomes)
  3. Coding (Scripting using any data science tool or framework)
  4. Domain (Working knowledge on how a particular domain or industry works and overview of their process and operation works)

So, you can imagine how a data scientist is loaded with work! Data scientists address each of the areas of business that will not only make him more acceptable and employable but also help him brand as “Data Scientist”.

Working isolated

Numerous current and future data scientists will be "isolated" - working alone or in small groups inside a bigger organization. This isolation brings certain difficulties as well as opportunities. Drawing on my impressive experience both working in the elite athletics industry and instructing in the scholarly world, encounters to be experienced by recently stamped data scientists, and offers counsel about how to explore them. Neither the issues raised nor the counsel given is specific to sports, and ought to be material to a wide scope of knowledge domains. If you are the only employee (data scientist) in your organization with a certain data science skillset and access to raw data, then it can be awesome to try and preserve the unique attributes by keeping them to yourself. This might be true but it’s a short-sighted and self-limiting view. Working in such an isolated environment gives rise to two problems. First, it will make more work for you, because you have to do the fishing for everyone. Second, it will get you pigeonholed as the fisher, and because you are the only fisher you won’t be considered for promotion to the more lucrative position of Manager of Fish. Instead, what you should do is teach your colleagues how to fish. This will expand your influence and make the data science group into a larger entity—with you at the center of it.

Thus, it is easy to conclude that many data scientists will be isolated as these jobs require independence and self-motivation.

No clear salary benchmarks

Salary is one of the primary reasons people want to break into the data science field and come forward to make a full-time career. As per the salary reports on Glassdoor and Neuvoo, the numbers mentioned there showcase average salaries for the data scientists. It might be heartbreaking for many of you! But let me clarify here one point that these numbers vary from organization to organization. But not everyone can work in top MNC’s like Google, Apple, Amazon, etc. Now, these salary trends are becoming a regular occurrence. You need to prove yourself really a top data scientist with top industrial skills to get a job in Fortune-500 companies, then only you can save your career as a data scientist and expect a high salary in your account because small and mid-sized companies cannot provide much salary to data scientists.

What can you do to improve your job as a data scientist?

If we look back, 2019 was a great year for the data scientists and the data science job market. O hope, this trend is likely to continue despite the global pandemic. Everything is going to be virtualized and platformed and this will fuel the demand of data scientists in many industries. Let us now understand the areas to focus on so that you may lose your job as a data scientist-

  • Make a force-stuffed learning environment: This is fundamental for the individual and expert development of a person. This field is blasting with something new to investigate each day and at this pace, it’s basic to give a dynamic learning condition for data scientists.
  • Plan a solid Research and Development (R&D) team: Creating an R&D team can empower quality examinations that can be embraced in the field. Empowering representatives to direct research on profound themes is a formula for greatness.
  • Benchmark their remuneration: Benchmarking pay will impart trust and give information researchers an affirmation that they are being paid by the best business gauges (justifiably, this is altogether harder to do).

Future of a data scientist

After realizing all the factors related to the future of a data scientist, career development in data science need to be more specialized. Data science is no doubt a broad catch-all title. But there are many other career paths that are evolved with it. Whilst we look for more specific career paths the bubble around data science needs to be bust. By specializing in the career path of data science means tackling the most critical business-related problems and complex challenges that can help in the growth of business and make revolutionary leaps forward.

End Notes

By the day's end, it truly comes down to realizing that top data scientists appreciate a fun, animating, and remunerating condition that is well-considered and sorted out. It may seem like a ton of effort to draw in and hold top data scientists, and, well, you are correct. Simply consider that an effective investigation activity will improve not only the main concern, yet in addition brand recognition, employee retention, and more. Whether you are attempting to turn into a business of decision to draw in top data scientists, or hold the ones you as of now have in this exceptionally competitive market, ensure you know the top propelling variables for a data scientist to change occupations and maintain a strategic distance from these entanglements.

Data science is the skill and technology that every industry is craving. Having a data science skillset in the current era means having a great demanding career option in your pocket. If you are also dreaming of becoming a data scientist then check Data Science training at Codegnan. We have trained hundreds of data scientists until now. 

The salary of a data scientist in India ranges from INR 365k per annum to 500k per annum.

Our data science training will help you master data science analytics skills through real-world projects in multiple domains like Big Data, Data Science, and Machine Learning.  The trending word of data science is waiting for you to be skilled.



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