Many firms' interactions with IT, data scientists, and data engineers are typically problematic in today's business environment. Why? One of the major causes for this is that data scientists rely too heavily on IT and data engineers to gain the tools or environments they need and to put work into production. As a result, data science work lags, deployment takes longer, and ROI suffers. What is the solution? Providing data scientists with the tools and resources they need to be self-sufficient, allows IT and data engineers to focus on other tasks.
According to a survey, "machine learning specialists topped its list of developers who indicated they were looking for a new employment, with 14.3 percent." Data scientists came in second with 13.2 percent." This data came from a survey of 64,000 engineers conducted by Stack Overflow.
Here are four major reasons why data scientists are unhappy in their professions:
Many new data scientists were attracted to the area because it allowed them to address difficult problems with cutting-edge machine learning techniques that had a substantial impact on business. This was a chance to feel like the work we were doing was more important than anything else we'd done before. However, this is not always the case. The most prevalent reason for data scientists to leave is that their expectations do not always match reality. There are various causes for this, and compiling an exhaustive list is impossible.
Because each company is different, many recruit data scientists without the essential infrastructure in place to gain the benefits of AI. This exacerbates AI's cold start problem. When you add in the fact that these companies neglect to hire senior/experienced data practitioners before juniors, you have the makings of a bad and unhappy relationship for both parties. The data scientist was probably hired to construct complex machine learning algorithms to provide insight, but they are unable to do so because their primary duty is to organize the data infrastructure and/or make analytic reports. In contrast, the firm just required a chart to present at their daily board meeting. The corporation is frustrated since they haven't seen anything.
The fact that expectations do not always match reality is the most common cause for data scientists to depart. There are numerous reasons for this, and they can't reasonably list them all, but this post is simply a summary of some of the ones came across. In contrast, the firm just required a chart to present at their daily board meeting. Because value is not being produced quickly enough, the firm grows annoyed, and the data scientist feels unsatisfied with their work.
This emphasizes the two-way connection between the data scientist and the employer. It'll only be a matter of time before the data scientist finds something else if the firm isn't in the proper position or has goals that coincide with the data scientists. Samson Hu has a terrific piece on how Wish's analytics team was established that is enlightening for anyone interested. Another reason data scientists are disillusioned is similar to why they were disillusioned in academia: they expected them to have a significant influence on people worldwide, not just within the corporation.
If you truly believe that having a large number of machine learning algorithms would make you the most important data scientist, consider my first point: expectation does not always correspond to reality. The reality is that the most powerful individuals in your company need to have a positive impression of you. That may involve performing ad hoc work all the time, such as pulling numbers from a database and giving them to the appropriate people at the right time or working on minor projects only to ensure that the right people have the proper impression of you. It was a vital aspect of the work, as annoying as it sometimes be.
Following on from doing whatever to impress the appropriate people, those same powerful individuals frequently don't grasp what "data scientist" means. This means you'll be the analytics expert, the go-to reports man, and the database guru as well. Non-technical executives aren't the only ones that make incorrect assumptions about your abilities. Other technical colleagues assume you know everything there is to know about data. It might be difficult to tell everyone what you truly know and control. Not because anybody will think less of you; rather, as a rookie data scientist with little industry experience, you will be concerned that others will. This can be a difficult situation.
Successful data products have well-designed user interfaces with cognitive skills and, most crucially, a meaningful output that consumers perceive as solving a relevant problem. Now, if a data scientist devotes all of their time to learning how to create and operate machine learning algorithms, they can only be a small part of a team that leads to the success of a project that yields a valuable product. This means that data science teams that operate alone will struggle to generate value! Despite this, many companies continue to employ data scientists who create their projects and write code to solve issues. In certain instances, this is correct.
