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Here’s Why so Many Data Scientists Are Leaving Their Jobs

When it comes to data science, the sky is the limit. The growing industry has several unexplored potentials and few roles, like data scientists and Artificial Intelligence (AI) engineers, that are taking over all other industries. According to Indeed, there has been a 29% increase in the demand for data scientists in just over a year. A survey conducted by IBM indicated that there will be 2,720,000 data science-related openings in 2020 alone! The Harvard Business Review calls it the “sexiest job” of the 21st Century. Yet, despite the growth in numbers and continuous demand, Glassdoor observes a dip by 1.2% in the salaries of data scientists since March 2019. Industry experts have also observed a mismatch in the supply and demand for data scientists. Here, we have analyzed why the budding industry is seeing a slump in its early stages. 5 reasons data scientists quit their jobs 1. Expectations vs. realityWith the growing demand and potential opportunities, it is not a surprise that individuals from different fields are looking out for opportunities in data science. Many take up these positions with a lot of expectations. Although the data science industry is touted as an open field where one can develop expertise irrespective of their non-technical backgrounds, the reality is quite different. According to IBM’s The Quant Crunch, 81% of data science jobs require workers with 3-5 years of experience or more. Aspirants are expected to know the basics such as collection and storage of data, version control, deploying models into production, and other key aspects. Not only does the lack of such training hamper the individual, but it also confuses them further about their job role. 2. Matching data with business goals One of the perils of being a part of a growing industry is the grey area of the unknown. Business leaders all over the world are expectant of the data’s results to revolutionize their businesses. With the data being collected, businesses intend to shape their goals to align with their customer/user behaviour or needs. However, a lot of data accumulated or being gathered is based on experimentation. This process of trial and error takes several months to arrive at the desired outcome, which delays the business requirements in due process.  It can be frustrating for individuals/teams to orient with such business needs and stress, causing them to quit.  3. Need for constant upskillingThe industry is entirely dynamic and demands new skills every day. Usually, individuals want to get their hands dirty to try the quickest and most effective method to get accurate data. But in most cases, this might lead to more delays in the data processes. Moreover, businesses expect data scientists to be in the know-how of their field, despite their workload. For example, Natural Language Processing (NLP) domain is Facebook AI's XLM/mBERT with a multilanguage model with 100 languages and many more features – a quick rise since its inception in 2017. Not all industries have a separate R & D wing to accommodate this or the flexibility for their data scientists to explore more. According to O'Reilly's 2019 artificial intelligence survey, almost 18% of its respondents said that the lack of skilled people in Artificial Intelligence is slowing its adoption in companies. 4. Compromising salaries While the reports show high figures for data scientists, not every company or job allows for an exact pay-out. Top companies like Pinterest and Uber shed over $212,000 per year but the median salary lies at $80,265. As many individuals are open to opportunities in the mid-level and small companies, the salary pay-out is considerably lesser. With the glaring gap in these figures and no proper demarcation/standardization of salaries, individuals find it frustrating to cope up with the industry.  5. Short term projectsMost of the small to mid-sized companies tend to look for data scientists only for short-term business requirements. By tweaking business protocols to match consumer trends, businesses are merely looking to get offshore instead of finding permanent solutions. This jeopardizes the careers of data scientists in these companies are they are usually employed only for an interim period. Sometimes, this can also mean hiring large teams and eventually reducing its size to fit the company requirements and budgets.  An added advantage to this is that individuals will develop a portfolio of projects instead of a single skillset. Freelancers in data science are also on the rise and expertise in multiple fields within data science is in demand.  The reality of data scientistsAccording to a report, Data scientist jobs remain open 45 days – 5 days longer than the average market. Specific job roles such as Director of Analytics and Systems Analysts tend to remain open for well over 50 days. With salaries hitting a roadblock and expectations of the aspirants unmatched by the industry standards, the gap in the demand and supply is expected to grow. If you are a data scientist or an aspirant, being open to new opportunities and exploring different projects is vital for growth. Industry leaders advise freshers to constantly engage with seniors or organization alumni to bridge the gap between expectations and reality. Despite rampant workplace politics and siloed work, individuals are encouraged to actively research and gain industry insights. Learning new skills in technologies like AI, ML (machine learning) are also some ways to bridge this gap. With a broader approach to data, data can also broaden a data scientist’s career.
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Here’s Why so Many Data Scientists Are Leaving Their Jobs

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Here’s Why so Many Data Scientists Are Leaving Their Jobs

When it comes to data science, the sky is the limit. The growing industry has several unexplored potentials and few roles, like data scientists and Artificial Intelligence (AI) engineers, that are taking over all other industries. According to Indeed, there has been a 29% increase in the demand for data scientists in just over a year. A survey conducted by IBM indicated that there will be 2,720,000 data science-related openings in 2020 alone! The Harvard Business Review calls it the “sexiest job” of the 21st Century. 

Yet, despite the growth in numbers and continuous demand, Glassdoor observes a dip by 1.2% in the salaries of data scientists since March 2019. Industry experts have also observed a mismatch in the supply and demand for data scientists. Here, we have analyzed why the budding industry is seeing a slump in its early stages. 

5 reasons data scientists quit their jobs 

1. Expectations vs. reality

With the growing demand and potential opportunities, it is not a surprise that individuals from different fields are looking out for opportunities in data science. Many take up these positions with a lot of expectations. Although the data science industry is touted as an open field where one can develop expertise irrespective of their non-technical backgrounds, the reality is quite different. According to IBM’s The Quant Crunch, 81% of data science jobs require workers with 3-5 years of experience or more. Aspirants are expected to know the basics such as collection and storage of data, version control, deploying models into production, and other key aspects. Not only does the lack of such training hamper the individual, but it also confuses them further about their job role. 

2. Matching data with business goals 

One of the perils of being a part of a growing industry is the grey area of the unknown. Business leaders all over the world are expectant of the data’s results to revolutionize their businesses. With the data being collected, businesses intend to shape their goals to align with their customer/user behaviour or needs. However, a lot of data accumulated or being gathered is based on experimentation. This process of trial and error takes several months to arrive at the desired outcome, which delays the business requirements in due process.  

It can be frustrating for individuals/teams to orient with such business needs and stress, causing them to quit.  

3. Need for constant upskilling

The industry is entirely dynamic and demands new skills every day. Usually, individuals want to get their hands dirty to try the quickest and most effective method to get accurate data. But in most cases, this might lead to more delays in the data processes. Moreover, businesses expect data scientists to be in the know-how of their field, despite their workload. 

For example, Natural Language Processing (NLP) domain is Facebook AI's XLM/mBERT with a multilanguage model with 100 languages and many more features – a quick rise since its inception in 2017. Not all industries have a separate R & D wing to accommodate this or the flexibility for their data scientists to explore more. According to O'Reilly's 2019 artificial intelligence survey, almost 18% of its respondents said that the lack of skilled people in Artificial Intelligence is slowing its adoption in companies. 

4. Compromising salaries 

While the reports show high figures for data scientists, not every company or job allows for an exact pay-out. Top companies like Pinterest and Uber shed over $212,000 per year but the median salary lies at $80,265. As many individuals are open to opportunities in the mid-level and small companies, the salary pay-out is considerably lesser. With the glaring gap in these figures and no proper demarcation/standardization of salaries, individuals find it frustrating to cope up with the industry.  

5. Short term projects

Most of the small to mid-sized companies tend to look for data scientists only for short-term business requirements. By tweaking business protocols to match consumer trends, businesses are merely looking to get offshore instead of finding permanent solutions. This jeopardizes the careers of data scientists in these companies are they are usually employed only for an interim period. Sometimes, this can also mean hiring large teams and eventually reducing its size to fit the company requirements and budgets.  

An added advantage to this is that individuals will develop a portfolio of projects instead of a single skillset. Freelancers in data science are also on the rise and expertise in multiple fields within data science is in demand.  

The reality of data scientists

According to a report, Data scientist jobs remain open 45 days – 5 days longer than the average market. Specific job roles such as Director of Analytics and Systems Analysts tend to remain open for well over 50 days. With salaries hitting a roadblock and expectations of the aspirants unmatched by the industry standards, the gap in the demand and supply is expected to grow. 

If you are a data scientist or an aspirant, being open to new opportunities and exploring different projects is vital for growth. Industry leaders advise freshers to constantly engage with seniors or organization alumni to bridge the gap between expectations and reality. Despite rampant workplace politics and siloed work, individuals are encouraged to actively research and gain industry insights. Learning new skills in technologies like AI, ML (machine learning) are also some ways to bridge this gap. With a broader approach to data, data can also broaden a data scientist’s career.

KnowledgeHut

KnowledgeHut

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KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com

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