According to a report, data scientists spend two hours a week searching for new jobs

'I'm a data scientist,' feels pretty prestigious to say this, isn't it? Then why is there a downward trend recently in data science professions, and especially, among data scientists? Lately, for over a couple of years, They are quitting their jobs from top technology companies. Despite getting paid handsomely, they choose to walk out on many scenarios. The worst case is that most of them don't even complete a whole year in the company. Let us go through the details to see what is pressuring data science professionals to the extent that they prefer leave rather than stay.

Data science is a vast field. Among them, data scientists deal with a lot of functionalities. With data becoming the core of all business decisions, companies, especially, top technology companies are looking for professionals who can transform a sea of data into actionable insights. The role of a data scientist involves working closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. Business data science professionals design data modelling processes, create algorithms and predictive models to extract the data the businesses needs. Besides being professionally successful, They are seen as people with superpower when it comes to data. Data scientist was named as the 'sexiest job of the 21st century' by Harvard Business Review not long back. Starting from Fortune 500 companies to retail stores, organizations around the world want to build a team of top data science professionals to drive their company towards success.

Despite getting a lot of attention for a long time, the positive trend is taking a u-turn in recent years. According to Financial Time's investigation, They are spending an average of two hours a week looking for a new job. While machine learning specialists topped the list of developers who said they were looking for a new job, at 14.3%, data scientists followed the trend with 13.2%. If you are a data scientist aspirant or a data science professional looking to switch jobs, please read this before you leap.

When expectation hits reality

Business data science is a vast area where many actions take place. Even though the scope for data science is extremely high here, data scientist's expectations and the reality of the working environment contradict with each other. The gap between the expectation and reality is so void thats why they prefer to leave the space at some extent. But the expectation gap also varies from one data scientist to another based on their experience and mindset.

For example, aspiring data science professionals are self-learnt and have gathered their knowledge through online courses and books. But the top technology companies look for practical knowledge from them. When fresh data scientists are leashed out in the rough world, they realize that they have no idea about certain important things like machine learning pipelines, the role of software engineer in data scientists' skillset, deploying a model, importance of data cleaning, etc. Besides, aspiring data scientists might feel the enthusiasm to play with machine learning tools, but the actual field of data science demands more on collection and storing of data and how to perform version control and deploy models in production, which are comparatively less interesting.

The perception of 'knows all about data' hits hard

In the business radar, especially among non-technical employees working in the field, data scientists are seen as an answer box. Particularly, company executives with no tech background believe that data scientists are a remedy for all emerging data problems. They envision data science professionals as analytics and database expert and a go-to-reporting guy. Top technology companies have a blind assumption that data scientists know everything like Sark, Hadoop, TensorFlow, bla, bla, bla. But the reality is that no data scientist can be expert in all the solutions. They too are human beings who have limited knowledge. The thoughtless perception of data scientists makes them run away from the profession they dreamed of ruling one day.

Getting investment for projects is troublesome

Even though business data science is a field where you can seek for answers, they are experimental projects, to begin with. No data scientist can go through the data of a top technology company and come up with insights right away. Data science projects require a lot of trials and error for project to succeed and this could even take months. Besides, business data science projects require a lot of investment. Convincing company executives to fund the project is one thing, but consoling them if the project is not productive is another tougher task. When data scientists go through such rough times continuously, they become frustrated and prefer to leave the job for good.