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4 URGENT Problems that lead to Data Scientists to quit their jobs

By Vahid Haghzare, Director Silicon Valley Associates Recruitment &
Armae Garcia, Marketing Associate, Silicon Valley Associates Recruitment 

 

 

Data Science is in High Demand

According to most IT Recruitment Agencies in Singapore at the start of the year, Data Scientists were expected to be in higher demand than ever before in 2022. And as we are well into the year now, the predictions seem accurate- Silicon Valley Associates Recruitment is finding there are few careers as appealing and in-demand currently as that of the Data Scientist.

LinkedIn considered the data scientist career as one of the best careers for professionals today. Large corporations, small businesses, and startups alike all seek to hire talented data, and science professionals.  

A recent report by the United States Bureau of Labor Statistics suggested job opportunities for data scientists are expected to increase by more than 39 percent this year. The demand for data scientists was already on the rise with a 29% increase in 2021 and is forecast to increase 27.9 percent by 2026- that will be over 11 million Data Science jobs. 

People outside of the data science field often view data scientists as if they were possessing special abilities. One factor that adds to this is media hype. 


Dissatisfied Data Scientists

But although demand for Data Scientists has increased, SVA Recruitment has also noted that many DS professionals are actually leaving these high-paying jobs as well this year at an alarming rate, and it may be about to hit your organization as well.    

What is causing this widespread spike? 

Our IT Recruiters at Silicon Valley Associates Recruitment have listed some of the common complaints that are motivating Data Scientists to quit their jobs

 

See Also: 4 Alternative Jobs for Data Scientists in 2022


Problem 1: Huge gap between Expectation and Reality.

A common issue amongst the Data Scientist candidate community our IT Recruiters speak to, there is a growing gap between data scientists' assumptions about what they should be doing and what they actually do. 

One example of this is the practice of self-training in data science. Many who call themselves data scientists, have acquired their skills through books and videos, but

have limited experience with using data to solve practical problems such as:


•    The functions of a machine learning pipeline
•    Cleaning data can be time-consuming, and it is an important part of the data analytic process.
•    Significance of software engineering as a data scientist
•    To put a model into production/deploy a model means to make the model operational.


Newcomers and seasoned professionals alike are drawn to the opportunity to experiment with innovative machine learning methods and advanced frameworks. 
In practice, the data science industry is far more complex than courses and tutorials might suggest. There are too many variables at work for a data science project to be similar to what we see in online events.
The company wants you to be able to process, store, and manage data; create and maintain version control, and deploy models.

Mismatched expectations between data scientists and their employers are a key reason why many data scientists leave their jobs.
 
Solution: 

A good way to bridge the gap between expectations and outcomes is simply communication. SVA Recruitment has found that new data scientist’s simply asking their more experienced colleagues and managers for advice and expectations and having frank open conversations about the reality, can lead to dramatic improvements in workload, manager’s anticipation, and employee happiness. 
See also: Hottest Trends on Data Science Jobs in 2022


Problem 2: Insufficient formal training and mentorship

With the pace of technological change, we suggest that data science is a field for people who enjoy taking on new challenges. Think of NLP as an example, the field of Natural Language Processing has seen tremendous change in the last few years.

Most data scientists are eager to work on the most advanced methods and frameworks. While a data scientist's role is ever-evolving, it is not insusceptible to sluggishness. 

For most people, after a given amount of time, they will experience a sense of stagnation, and they will always want to take on new challenges.


Solution:

As a Data Scientist, you should seek to learn more about the field by exploring additional areas of specialization. For example, a computer vision specialist interested in learning about natural language processing could benefit from working in an R&D zone, where businesses often lack such resources and have a greater need for them. 

Because many companies lack the infrastructure necessary to support a data scientist's work, such as computing systems and access to tools, it is difficult for firms to hire data scientists. The company may have limited capacity to process data. At some point, the amount of data may overwhelm a data scientist's ability to find useful patterns in it.


See also: 5 Ways for Data Scientists to Keep Coding and Growing

 

Problem 3:  Leveraging Data to help achieve business objectives.


One challenge arises when a company tries to prove that it is at the cutting edge of technological advancements. This has been made possible by recent interest in artificial intelligence and data science.


Business executives have been turning to artificial intelligence as a solution for their business troubles. They will find an answer quickly if they engage in AI and work with the right experts.


Data science technique generally includes trial and error procedures and multiple tests before reaching a final result. The process can take months or even years.

 Although investment in artificial intelligence and data warehouse infrastructure is required to ensure the success of these endeavors, it can be difficult to extract useful insights from vast amounts of data; a reason why data scientists need flexibility in their workday, the ability to work on data at a time and place that works for them.


IT Companies often struggle to retain data scientists, who typically leave when their seniors set unattainable goals. SVA Recruitment’s tech recruiters see this happen when companies set unrealistic expectations.


See Also: Mentorship: The way forward for Data Scientists


Solution: 

Create a quantitative performance matrix to help track data scientists’ progress.  Agility is a valuable trait for data scientists who aim to get the most out of their resources.  Business leaders’ intuition and knowledge can help data scientists make more informed decisions. 

Communication between the data science and business teams is crucial. They must coordinate their efforts, and they must be unified in their goals and vision.


Problem 4: Platforms and Projects

Currently, Data scientists can work either in-house or as freelancers, and their skills might include Spark, SQL, Neo4J, Hadoop, Hive, Pig, MySQL, Python, R, Scala, and TensorFlow. They may also be familiar with natural language processing (NLP), machine learning, and artificial intelligence (AI).

The majority of these courses cannot be offered to resident data science professionals for logistical and project-related reasons.

Solution:

How can companies keep their data scientists from leaving? The following proven methods can help companies retain their data scientists:
•    Provide formal training and certifications
Providing a progressive and challenging learning environment for data scientists is important.

Companies must provide opportunities for employees to develop their skills and abilities, such as through on-the-job training or participation in external certification programs.

•     Form a strong R&D team
The right research and development team can help you do top-notch research in your field. Allowing employees to perform thorough research is a recipe for success.
Data scientists must learn about the field in which they are working. This allows them to ask questions of management and ensure that expectations are in line with the project’s probable outcome.


See also: How to Start Off Your Career as a Data Scientist

 

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Silicon Valley Associates is a specialist IT Recruitment Agency ideally positioned to support the continual demand from tech companies and IT Departments looking to hire in Hong Kong, Singapore, Shanghai, Dubai, Japan, and Worldwide. Please let us know if you would further advise on the above topic or your hiring needs

 

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