Data Science: Reality vs Expectations

In the majority of companies, the executives in charge of data science and the decision-making process using data science, have little or no education or understanding in actual data science. Where does this leave you, the data scientist?

When you tell people you’re a Data Scientist, it comes with a lot of assumptions and high expectations. Each company has its unique definition of what a Data Scientist does, this is dependent on their expectations. 

I dived into the world of Data Science after completing a 9-month Bootcamp in September 2019. The curriculum included Linear Algebra, Statistics, Applied Modelling, SQL, Machine Learning, Computer Science, and Production Deployments (e.g. git, Flask, AWS). I was excited to be part of something that was growing at a ridiculous rate. Tesla had self-driving cars, Instagram was creating crazy algorithms, and nearly every company was implementing the use of data in their business plans. 

After having completed the Bootcamp, I thought I was going to be given large amounts of data, where I would need to find patterns between the variables. There was going to be a lot of data cleaning and implementing machine learning models to gain a better understanding of current and future outputs. I was seeing vacancies for Data Scientists in nearly every sector, from the obvious, like Amazon to newbies, like major skincare brands. I believed I was going into a career that every company needed, and although that is true, the reality was different.

I had many assumptions and expectations of what the role of a Data Scientist entailed. However, in my recent years in the industry and also speaking to other Data Scientists, Machine Learning Engineers, and Analysts, I started to see ways in which data science has failed to meet the expectations in the industry.

Below I will go through each point. 


1. Lack of Data Science Leadership


Data Science: Reality vs Expectations
Jehyun Sung via Unsplash


I believe that in order for anything to be successful, you need to have the right team, where each member has a specific skill set. When all the member's skills come together, there is a high chance of success. 

In the majority of companies, the executives in charge of Data Science and the decision-making process using data science, have little or no education or understanding in actual Data Science. 

Where does that leave you as a Data Scientists? When you are going through the Data Science workflow, there are going to be times when you’re blocked or need advice from an expert in the field. Speaking to executives with no knowledge in Data Science will make your work life difficult as the responsibilities will fall onto you to resolve it. 

This becomes a major problem when companies implement the top-down approach, which is when the decision-making process occurs at the highest level and is then communicated to the rest of the team. This approach is still heavily implemented in a lot of data-driven companies, leading to hierarchy and the overall satisfaction of the workforce. As a Data Scientist, you may not have a seat a the table or be valued and respected enough to be a part of the decision-making process. You will be very frustrated as it was your time and energy that went into cleaning the data, building models, and producing accurate outputs. 


2. Lone Wolf: Not by Choice

This point is related to the one above. There has been a drastic rise in data-driven start-up companies over the last decade. Some start-ups do well from the jump and build efficiently, however, the majority have <100 employees. 

Once you exclude the executives, managing directors, HR team, and more, there are only a few employees who are proficient in data analysis, data visualisation, machine learning, and SQL. If you are one of the primary Data Scientists on the team, there is a high chance you will be overwhelmed with the multiple requests from different team members. 

In cases like this, there is no harm in saying no to these requests. There comes a point where too much work starts to affect you and your well-being. At this point, the company should understand the growth of the company and start looking into expanding the data team. 


3. Data Scientists Don’t Know Everything


Data Science: Reality vs Expectations
Patrick Tomasso via Unsplash


The interview process for a Data Scientist is normally very complex. Have a look at simplilearn, to see the Top 60 Data Science Interview Questions and Answers: Basic to Technical. In the past, I’ve been asked questions about SQL, Linear and Logistic Regression, Decision Tree, and more. 

You can imagine how daunting it is to prepare for a Data Science interview, you need to know a wide range of concepts in and out! However, don’t let this deter you from pursuing a career in Data Science. The more context you know, the better chance you have. Keep going and practising the top interview questions till you feel confident. You can ask your hiring managers for details of the interview process, which will help you prepare. 

Once you land your job as a Data Scientist, you may be asked to solve a problem, with minimal or no direction. This can be due to it being requested by members of the team that have no knowledge of Data Science, or because of the misconceived notion that Data Scientists know everything. My advice to you is to work closely with others in the data team, such as software engineers and analysts to support you through this. If you are unfortunately the only data person on the team, having an expert or highly proficient mentor to advise you is a good stance to take. 



The aim of this article was not to discourage anybody from pursuing a career in Data Science, it was to give people a better understanding of what the job may entail. 

I wish somebody had given me the breakdown and advice before I went into my first commercial Data Science job. It’s good to learn these things on the job, it helps you grow. But it can make your life easier when you can take the knowledge with you and be prepared. 

This article aims to also help companies understand where they may be going wrong and how they can improve. A company’s overall goal is an efficient and successful company, and for me, that starts with your employees. 

Nisha Arya is a Data Scientist and freelance Technical writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.