1. Employers are often looking for a unicorn data scientist

Everyone working in the field is well aware that the skill set a data scientist brings to the job can vary widely depending on the context. A combination of statistical background, coding knowledge, and business acumen are definitely required, but to find an individual that will have expert abilities in all three domains is very rare. Despite this fact, some employers explicitly look for such job candidates (ideally with a PhD and perfect soft skills on top, possibly even farting rainbows). These expectations can be very unrealistic at times (although we at Profinit have managed to hunt down some unicorns).

2. The most sought-after programming language is Python, not R

When we checked the global data science job postings on Indeed.com for language requirements, Python came out as the most frequently required skill. However, the truth is that, ideally, a good data scientist should be familiar with both of these languages. Being able to work in both Python and R enables you to harness the power and combine the advantages of both, depending on the task at hand. While Python was created as a general multipurpose language to improve code readability, R is deeply rooted in statistical analyses and academic research.

3. Data scientists based in London get paid much less than their US counterparts

It seems like the locations of global tech hubs are slowly shifting. Nevertheless, the United States remains a centre for digital technologies. In Europe, London is generally viewed as the competing tech counterpart. However, if we compare the remuneration of data scientists in New York and London using data from glassdoor.com, the salary gap is significant. The average salary for NY-based jobs is $115,815 a year, (as of 8th July 2019), whereas in London it is only $63,779 a year, (as of 8th July 2019). It has to be said that this does not factor in health insurance, paid leave, or expected working hours (London might come out more favourably on these fronts). Anecdotal evidence from, for example, reddit.com also suggests that at least financially speaking, for data scientists the grass is greener on the other side of the pond…

4. It can be tricky to keep a data scientist

Many data scientists don´t stay in a company for too long. As jobs are often project-based and the shortage of experts is palpable, people are not afraid to accept better and more exciting job offers. As a result, employers need to go the extra mile to attract quality data scientists over the long term. This situation is very favourable for free spirits in the field who want to give digital nomadism a shout. However, with solid workplace culture and interesting projects we at Profinit manage to keep our team happy in the long term. Get in touch to hire their expertise!