Data Science, the sexiest job of the 21st century. A description that propels one to halt whatever it is they’ve been doing to revise long-term career path. Sheer grit and determination goes in to development, refinement, and self-marketing to boost prospects landing a role within industry. However, as in many cases, before the sexy life comes the ugly life. Endless job applications, networking on social media and in person (prior to the pandemic) to no prevail, for many months in the majority of cases.
Suddenly, a glimmer of hope stems from within as interest arises just before the ego is completely diminished. An opportunity to end the long suffering and finally realize all the effort that has been put in over the past few months of refining yourself.
STOP! It is at this point many make the decision of doom.
You may want the Sexiest Job of the 21st Century, but if you’re not careful you may only receive a title of honor, "Data Scientist", rather than the actual job.
Backstory
Times have changed and as it happens, technology has moved. Artificial Intelligence is becoming more and more democratized each day for companies other than Facebook, Amazon, Netflix and Google to enter what is likely to transform life as we know it. It is clearer than ever now that C-Suite must understand and leverage the trends in automation and Artificial Intelligence.
In all honesty, many are definitely beginning to pay attention. From healthcare, to retail, and in sports, teams are hastily working on how they can adopt Artificial Intelligence to become better at what they do. Nonetheless, as with most of fast-advancing technologies, AI has inspired major FOMO, FUD and feuds to say the least.
"Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…" — Dan Ariely
These factors have contributed to immense skepticism about the potential of AI, as well as some key misjudgments. However, these misjudgments do not only exist in the ethics of AI, but also in a manner that severely affects aspirants keen on entering the field such as many having inflated expectations of AI.
People working in this field experience many frustrations, says Goldbloom. Bad data are one of the main ones: their employers cannot provide the essential raw material for them to obtain results. Some also complain of being given a lack of clear questions to answer. Companies may sense the opportunity, but they often do not know enough to get the most from their data assets. This also highlights the lack of technical knowledge among non-specialist managers who work alongside data scientists and machine learning experts.
An extract from How Machine Learning Creates new Professions – and Problems (2017). Financial Times
There are many factors that go into why Data Scientist are leaving their jobs, but if we have the foresight we can assess those factors and take control over what we can control then it is more likely to reduce the churn rate.
Jonny Brooks-Bartlett created an in-depth story in 2018 titled Here’s why so many Data Scientist are leaving their jobs, detailing exactly what the title states. Therefore, I will not attempt to reinvent the wheel instead you may want to read his story – I strongly recommend that you do!
Bridging the gap between Expectation and Reality

We all have aspirations. For us to progress towards self-actualization in Maslow’s Hierarchy of Needs one of the steps requires that our safety needs are met, which includes job security.
Assessing whether a company’s vision compliments the aspirations we have for ourselves may be difficult via online search. However, when granted with an opportunity to sit with the employer we must be able to ask questions that we may find uncomfortable. In other words, an interview is not a mere test to see whether you, the interviewee, have the hard and soft skills required fill the vacant position. An interview should instead be approached by an interviewee as a 2-way conversation – a test of compatibility. In essence, the interviewer is seeking to fill a role that may likely advance the company and the interviewee is attempting to satisfy the next stage on their ascent towards self-actualization.
Understanding where the employer is in their adoption of Artificial Intelligence is also a crucial factor in this phase, since whatever they lack may fall into your list responsibilities. Therefore, ask the questions! "Have you got a team of Data Scientist working on any interesting problems?", "Are their senior/experienced Data Scientist on the team?". "Do you have data infrastructure in place?".
The purpose of asking questions is to define clearly what exactly it is that you’d be required to do for the company if they were to hire you. Remember a company that is adopting a new technology do not know what they do not know about the new technology and leaving important questions relating to what the company is already doing in their adoption of AI unanswered may have a severe impact on what your role actually requires – in turn affecting your satisfaction in a role.
Navigating Corporate Politics beforehand

Lets face it, politics reigns in organisations. Every company have their own culture and protocols. Though we may never be able to fully understand this prior to entering in an organisation, today’s advances in communication makes it easy to gain a general overview of how a company operates.
Expecting to walk into a company and change the whole company is to some degree quite delusional. If things have been working for a long time before you, people may not be so susceptible to change which may well be justified, even though history has shown it to be a long-term killer of business.
Before taking on a role at a company, research should not only be conducted on what the company does, but also on reviews from the people they serve, past employees and current employee’s. Yes, If possible reach out to them all. If a company was unwilling to work with customers effectively to ensure their needs were met, or if multiple employees leave because they believe their contributions were not being heard then serious thought must go into whether that’s the environment you’d like to work in.
Artificial Intelligence still not fully being developed in industry means that there is plenty of uncertainty. Many do not trust it out of ignorance and some have valid reasons – like jobs being replaced by AI. Therefore, a large proportion of the job may be to convince other teams of the benefits of AI by doing small task that can boost trust of people in the workforce. However, if a company’s culture, judging by how they treat past customers and employees, does not reflect adaptability to change then it is likely you will be miserable in this environment.
Of-course your ambitions behind communicating with people to gain insight is not to gossip about the company (avoid this at all cost), your aim is simply to reduce the element of surprise that may occur upon entering the company. Rather than holding the company to your own expectations, do your research as much as possible beforehand to build an image of what you may potentially face if you were to take the role.
Wrap Up
The prerequisites required to attaining a Data Science role that genuinely want fall heavily upon the person aspiring to break into industry. It’s unlikely that you’d have a good picture of what would be required of you in industry if you’ve never been in the industry, so it is imperative that you build up this image by reading various blogs and job specs that detail the roles and responsibilities of a Data Scientist – also speaking to Data Scientist in the industry is useful.
Although we may never truly grasp what may be in store for us at a particular company before going in, it remains our responsibility to gain as much information possible to design a detailed mental image of what work at that company may be like. As we build up this mental image we can use this image to determine what sort of job may be right for you in the long run and seek companies that we believe may be able to fulfil this criteria. This does not guarantee that we will get it right the first time, but it does reduce the chance we will get it wrong – and if we do get it wrong there is nothing wrong with failing as long as we can learn from the mistakes we made.
If there is anything that you think I have missed, something that you’d like to point out to me, or if you are still unsure about something, your feedback is valuable. Send a response!
However, If you’d like to get in contact with me, I am most active on LinkedIn and I’d love to connect with you also.
Other stories you may like:
Staying Motivated For Your Data Science Career
How to Learn Faster for Data Scientist
The Reason You’re Frustrated when Trying to Become a Data Scientist