The COVID-19 pandemic brought huge changes in the workplace, such as a massive increase in remote working. These may or may not last. However, one pandemic-driven change shows no signs of stopping: the Great Resignation.
When the pandemic first hit, resignations as a proportion of the workforce fell dramatically. However, they have since risen rapidly to record highs, far higher than the norm for the previous 20 years. This has been driven by people seeking a better work-life balance, or the ability to work from home more often, or simply rethinking their careers and goals.
We recently discussed how this is affecting data science in a SASChat. You can read the full tweetchat here, but here are my top takeaways.
Data scientists probably want what everyone else does from their jobs
A1: I think it comes down to three things: 1. They want to care about what their organization is doing. 2. They actually want to do modern data science and 3. they want the opportunity for personal development to keep up with the field. #SASchat #DataScience https://t.co/UdHtu1Ag33
— Jannic Horst (@JannicHorst) February 4, 2022
Data scientists are no different from anyone else in what they look for from a job. They want to feel that their job is worthwhile – both in terms of what they do for the organization, and what the organization does in the world. They want to be able to use their skills, and they want to be able to develop those skills further. And they also want to have fun and enjoy spending time with their colleagues and team members.
If there is one quality specific to data scientists, it is curiosity
A1. Working in a team with complementary skills and experience, a culture that stimulates curiosity and innovation. #SASchat
— Pascal Lubbe (@LubbePascal) February 4, 2022
Data scientists tend to be curious. They like to ask questions and find the answers to those questions. This means that they want to be in an environment that encourages curiosity and innovation, to enable them to explore new ideas. Ideally, this also needs a diverse team because more diverse teams generate more and better ideas – and plenty of data. Finally, curiosity is even more rewarding if you can see how it generates business value.
Companies can help by defining jobs more clearly
A3: As data science has become such a trending theme or even buzz word, it gets more misused in job postings to attract top talent which leads to disappointing experiences on the job. A disconnect between expectations and the reality of the role at hand. #SASchat #Datascience https://t.co/zYKfmwsHCO
— Jannic Horst (@JannicHorst) February 4, 2022
Data science is now a huge buzzword. Everyone (and every company) wants to think that they are using it – and that they need data scientists. However, this may not be the case. Clearly defining roles, including in job advertisements, means that people are recruited with a stronger understanding of what is expected – and are therefore a better fit for the job.
Unfortunately, not every company can offer the right environment for data science
A2. Many companies do not yet have a well established data driven environment. #SASchat
— Pascal Lubbe (@LubbePascal) February 4, 2022
One reason why some companies are finding it hard to recruit and retain data scientists is because they do not have the right environment in place. For example, they may not be data-driven, or they may be very focused on projects with a strong return on investment – which is often difficult to establish for data science work. Worthwhile work and development opportunities also vary considerably between organizations.
Companies can work creatively to establish this environment
Every role is different in terms of scope. I think where I've seen success in terms of Data Science satisfaction and retention is where teams work horizontally having the freedom to understand the wider business and contribute towards different use cases. #saschat #DataScience
— Harry Snart (@HarrySnart) February 4, 2022
There are ways for companies to establish an environment that encourages curiosity. For example, some companies allow data scientists to work in loose groups and move horizontally to work across teams. This gives people more opportunity to work on projects that interest them, and a better understanding of the broader business.
Analysts may also act themselves to improve their jobs
A2: or is the data scientist maybe the translator as McMinsey is asking? #saschat #datasciencehttps://t.co/HSYdzgbYgk
— Dr.Kaselowsky (@DrKaselowsky) February 4, 2022
https://twitter.com/DrKaselowsky/status/1489604064249851908
One of the biggest challenges in data science is that data scientists become frustrated that companies don't use their work. Some analysts may take matters into their own hands and extend their role into the realm of analytics translation. This can both make their job more interesting and also ensure that they and other data scientists see their work is appreciated.
The skills requirements are changing for data scientists
I think most employers recognise the need for analytics, the question is more around how to influence and persuade with the data and bring the decision makers along with you - does the leadership of a company rely on data to make decisions #saschat #datascience
— GlynTownsend (@GlynTownsend) February 4, 2022
As more analysts move into "translator" territory, there is a growing emphasis on the importance of business and communication skills for data scientists. It would be fair to say that most companies recognize the need for analytics. However, the real question for analysts looking to provide business value is how they can persuade those around and above them of the meaning of the data.
If you are interested in finding out more, check out the new cross-industry report "The impact of increased digitization on the data science field". It reveals the results from a study conducted last year among data practitioners, experts, and thought leaders. The report highlights the findings from a global survey on the impact of the pandemic on the day-to-day work for data professionals – and, ultimately, how they’re facing a new set of challenges while finding opportunities and new ways to work with data.
