Why So Many Data Scientists are leaving their jobs, 4 Most Important Reasons

By Kimberly Cook |Email | Nov 18, 2018 | 149160 Views

Voted as the sexiest job of the 21st century, following a career in data promises a high income and - due to the talent shortage in the current market - the upper hand with employers. So why is it that data scientists typically "spend 1-2 hours a week looking for a new job"?

Here are 4 main reasons data scientists may become dissatisfied with their jobs.

1. Expectation does not match reality
Data professionals choose the career path of data science for the complex problem solving, the cool new machine learning algorithms and the expected impact their work will have on real business problems. However, this is often not the case.

A large number of companies hire data scientists without a suitable infrastructure in place to start getting value out of AI. On top of that, many of these companies fail to hire senior/experienced data practitioners before hiring juniors.

This a recipe for a disillusioned and unhappy relationship for both parties. The data scientist has to sort out the data infrastructure and/or create analytic reports, instead of writing smart machine learning algorithms to drive insight. The company does not receive the expected chart that they could present in their board meeting each day. This ultimately leads to frustration as they don't see value being driven quickly enough. It is quite clear how all of this leads to the data scientist being unhappy in their role.

There should be a 2-way relationship between the employer and the data scientist. If the company isn't in the right place or has goals aligned with that of the data scientist then it'll only be a matter of time before the data scientist will find something else.

Another reason can be that data scientists sometimes are disillusioned on the impact their work will make. If the company's core business is not machine learning, it's likely that the data science will only provide small incremental gains. These may, of course, add up to something very significant or in less common cases data scientists may be lucky to stumble on a gold mine projects.

2. Politics reigns supreme
Practitioners sometimes believe that knowing lots of ML algorithms will make them the most valuable data scientist. Many times, this is not the case.

Keyholders need to have a good perception data scientists. This may translate to ad hoc work such as getting numbers from a database and having to do simple projects. It is a necessary part of the job and it can be very frustrating for skilled data professionals.

3) You're the go-to person about anything data
It is quite common that many stakeholders may not have a clear idea what a data scientist is and assume they know everything data related. As a result, expect the data scientist to be the analytics expert, the go-to reporting person, and the database expert too.

Data scientists are many times expected to know their way around Spark, Hadoop, Hive, Pig, SQL, Neo4J, MySQL, Python, R, Scala, Tensorflow, A/B Testing, NLP, and anything machine learning. They are believed to have access to ALL of the data and answers to ALL of the questions in no-time.

Explaining what the data professionals actually know and have control of, can be a challenge. One of the key reasons being afraid people would think less of them. This can be a quite difficult situation.

4) Working in an isolated team
Many companies still have data scientists that come up with their own projects and write code to try and solve a problem. But, data scientists that work in isolation will struggle to provide value.

When we see successful data products we often see expertly designed user interfaces with intelligent capabilities and a useful output which is perceived by the users to solve a pertinent problem. In reality, it involves many different skills that should not be expected for the vast majority of data scientists. As a result, if the project is taken on by an isolated data scientist it is most likely to fail - or take a very long time.

To conclude, being an effective data scientist involves understanding how hierarchies and politics work in business. Finding a company that is aligned with your critical path should be a key goal when searching for a data science job that will satisfy your needs. However, you may still need to readjust your expectations of what to expect from a data science role.

Source: HOB