Despite the growing demand for data scientists with handsome salaries, many professionals have quit their jobs. Is it a lack of skills or different expectations?
A The data scientist stock is currently at an all-time high. As we enter 2022, there aren’t many careers that can match the mystique, glamor, and respect that a data scientist commands.
I’ve seen non-scientific (or non-technical) people consider a data scientist to have superpowers. There are various reasons for this, including hype, but there’s no denying that the work of a data scientist is highly valued.
To back up these claims, I’ve listed some top job reports, I’m sure you guessed the job at the top of the list:
- More than 11.5 million data science jobs will be created by 2026, which is huge (The United States Bureau of Labor Statistics)
- There is a 29% increase in demand for data scientists (Indeed, 2021)
- The average entry-level data scientist salary is $93,167 per year (Glassdoor, 2022)
- Demand for skilled and skilled data scientists will increase by 2026, leading to a 27.9% increase in employment (The United States Bureau of Labor Statistics)
- Employer demand for data scientists will increase by more than 39% (IBM)
The above figures are staggering. From Fortune 500 companies to startups, organizations around the world want to establish a pool of talented data science professionals. Undoubtedly, the demand for data scientists is great, the salaries are competitive, and the benefits are plentiful, which is why LinkedIn has named data scientists as the most promising career.
Nevertheless, despite all these encouraging trends, there is a nagging sense of unease.
What causes this? Is there anything we miss?
Let’s review some of the top reasons data scientists leave their seemingly ideal jobs.
This is one of the most common problems in the data science world. The distance between what data scientists assume and what they do in the business is growing. A range of factors play a role in this, and they may differ from data scientist to data scientist. The gap between expectations is also determined by his level of expertise.
Take as an example the case of zealous data scientists. They are mostly self-taught and learned their skills from books and videos. They have limited experience with real-world applications and datasets. Personally, I’ve also met a lot of budding data scientists who had no idea:
- The importance of data cleaning, as well as the fact that it consumes a lot of time
- What is a machine learning pipeline and how does it work?
- What does putting a model into production/deploying a model mean?
- The Importance of Software Engineering in the Data Scientist Skill Set
Freshmen (and everyone else, to be honest!) are drawn to the opportunity to experiment with sophisticated machine learning techniques and state-of-the-art frameworks.
The truth is that the industry doesn’t work that way. There are far too many variables at play for a data science project to resemble what we see in online data science events.
The company wants you to know how to process and store data, how to effectively manage version control, and how to get your models into production, to name a few crucial features. This misalignment of perceptions is a fundamental obstacle that drives data scientists out of their jobs.
To bridge the gap between anticipation and reality, I always recommend newbies and novice data scientists to talk regularly to their seniors and company alumni.
Who doesn’t like taking on new challenges? Given the speed at which progress is being made, I would argue that the data science profession is ideal for these questions. Consider the field of natural language processing (NLP); the amount of change that has taken place over the past two years is incredible.
Most of the time, every data scientist would jump at the chance to work on these state-of-the-art methods and frameworks. Who wants to spend years building and iterating on a concordant logistic regression model? The role of data scientists is not immune to the element of stagnation. After a while you will hit a brick wall and the need for a new challenge will always be there.
Here are three important reasons for employee turnover that I have seen:
- Inadequate research and development: As a data scientist, you want to learn more about the field and broaden your horizons. For example, if you are a computer vision specialist interested in learning more about NLP, an R&D area would be the best place for you. Most companies lack it, which leads to attrition.
- Infrastructure deficit: Most companies do not have the infrastructure, such as IT systems, access to tools, etc., to support the function of a data scientist.
- commercial purposes: The operational capacities of the company can be limited and constrained. It may become impossible for a data scientist to derive more insight from the data beyond a certain point.
Here is another difficulty with unpopular expectations. This is mainly due to the recent craze around artificial intelligence and data science. Executives, CxOs, C-Suite executives, and investors all want to prove that their company or project is at the forefront of technological advancements.
We’ve seen many of these senior executives assume that AI is a panacea for their business problems. They will find an answer in record time if they commit to AI and the appropriate expertise.
Unfortunately, it is not the case. Before arriving at a final result, data science initiatives typically include multiple tests, trial-and-error procedures, and repetitions of the same process. The process takes months or years to achieve the desired result.
Although AI and data warehouse infrastructure require a significant investment (depending on the size of the business), work discoveries can take time because extracting useful information from large swathes of data may take time. That’s why data scientists need a flexible strategy that allows them to work with data at their own pace and location.
This is not welcomed by business leaders in a variety of fields. As a data scientist, I’ve seen this result in a massive influx of projects when data scientists are dissatisfied with the high expectations of their leaders.
Business leaders and data scientists should:
- Develop a quantitative performance matrix for the business to track data scientist performance progress
- Have agility because it’s crucial to get the most out of a data scientist
- Use the intuition and knowledge of business leaders to your advantage. For data scientists, this can be very beneficial
- Make sure data science and business teams communicate effectively. They must be coordinated and unified.
Which of these two possibilities would you most like to have:
- 🏄 A very flexible professional life in which you can work remotely and achieve significant personal growth?
- 🙇🏻 A 9 to 5 job where you must align your talents and results with company goals
The first choice is likely to be chosen by the majority of you. Who doesn’t love the freedom of work and the ability to choose what you want to focus on?
A data scientist today has a myriad of options to choose from such as freelancing (data scientists know Spark, SQL, Neo4J, Hadoop, Hive, Pig, MySQL, Python, R, Scala, TensorFlow, NLP and all regarding machine learning), personal branding, etc.
For obvious logistical and project reasons, most of them cannot be offered to resident data science professionals. To be honest, it’s an unavoidable cost of any business.
I strongly believe in the proven methods mentioned below, which can be applied by companies to retain their data scientists.
This is essential for the personal and professional development of an individual. This profession is exploding every day with new opportunities.
Speaking of the elephant in the room, providing a progressive learning environment for data scientists is essential. Employers should provide training and skills development opportunities through a variety of methods, including on-the-job training, data science certifications, and more. Some of the institutions that offer these certifications are:
Building an R&D team can help you conduct high-quality research in the field. Allowing staff to do in-depth research is a formula for success.
Given the immense competition and the need to high-level data scientists need to spend time learning the ins and outs of the field in which they work. This knowledge can be used to ask the right questions of management and ensure that their expectations are in line with the likely outcome of the project.