Harnham Netherlands recently partnered with a client that was hiring for two roles simultaneously: a Senior Data Scientist and a Data Analyst.
The Data Analyst role had a lot of growth opportunity, with the potential to evolve into a Data Science role, and was suitable for graduates without much commercial experience, while the Senior Data Scientist role required a few years of industry experience.
While working on both roles, our team found that many aspiring data scientists, who were relatively early in their data careers and thus didn’t have much commercial experience, wouldn’t consider the Data Analyst role as it didn’t involve machine learning or advanced data science techniques from day one.
They were very keen on the Senior Data Scientist role, but unfortunately many did not have the amount of experience that the client was looking for. So rather than take the analyst role, many passed up on the opportunity altogether.
Which made us wonder — is that the right move for someone early on in their career? Should aspiring data scientists say no to certain roles and hold out for their dream job?
This is a difficult question to answer. Harnham Director Ross Henderson shares his thoughts on this topic in the article below.
How flexible should early careers professionals be in their job hunt?
When should you take a role that will give you some relevant experience in the short term, and the role you want in the medium term, and when does it make sense to wait for a role that is 100% aligned with your career aspirations and guarantees that you’re on the right track.
The question feels particularly pertinent in 2023. This will go down as a year where the data job market was trickier than most. The start of the year saw mass layoffs, particularly from big tech, which coincided with hiring freezes and cautious spending in response to the global economy.
This year saw fewer job postings compared to previous years, and a focus on hiring professionals that can deliver from day one rather than early careers professionals that require training. Whether that is the right approach is a big question and too much for this article. I personally believe stretching talented early careers professionals is a great way of getting talent and results whilst keeping costs down—but that’s a topic for another day.
This year’s economic climate has left early careers data scientists in a difficult spot. They have often had to make dozens of applications, calls, and interviews and still can find it difficult to find a role. So, when the market is this tough, should you be flexible or be patient?
I put this question to my network of data professionals via a LinkedIn poll, and the consensus was clear! 850 data professionals in my network responded with the following results:
A staggering 64% felt that if someone is an aspiring data scientist, they should grab the opportunity of data experience rather than holding out for data science roles. A further 21% felt that aspiring data scientist thought that if a role has a data science path an early career professional should go for it. That’s 85% thinking those candidates should go for the more junior role rather than holding out.
The benefits of being flexible
- You’ll build on your data fundamentals: If you are an aspiring data scientist, getting fundamental experience in SQL, data visualisation, data engineering, or other data experience will be helpful down the line. In fact, we sometimes see experienced data scientists declined in recruitment processes if they’re missing those fundamental data skills.
- You’ll gain commercial experience: Once you are in a role, you may get the opportunity to work on side projects that can give you commercial experience in the role you want in the future. For example, if you are a data analyst, you can work with your manager to see if there are opportunities to apply machine learning as a side project and talk to other people in the business that might be working in that field. Once you have that experience, it will be easier to gain and be successful in interview processes or transition fully into that kind of role in that company.
- You keep the door open to other opportunities: You never know where you will end up. Careers in data can progress quickly, and it is not uncommon for a data professional to be the head of their department in the first 10 years of their career. Perspective of other roles in data can help you hire and lead in that space further down the line.
- Stand out in a competitive market: Particularly in data science, the job market can be competitive. It is an interesting and in demand skillset that lots of people want to get into. If you are interviewing against lots of other candidates, some of which are graduates, you will be able to stand out by demonstrating fundamental skills and a commercial awareness other candidates may lack.
When does it makes sense to wait?
Data science and AI is a broad field. There are some roles in data science that are highly specialised and rely on applied statistics or mathematics research degrees up to Ph.D. level, and particular types of machine or deep learning.
If you are interested in a specific field of that, and working in a research role, maybe you are better off waiting. I would still argue that roles that aren’t exactly what you want can add value to you career, but a foot in the door approach may not be right for you.
In summary, if you are an aspiring data scientist, particularly in the current climate, you should consider taking on roles that give you commercial experience in the right direction. It could make you a better data scientist and is likely to make you a better candidate for future roles.
Are you looking for a new opportunity in data? Get in touch with someone from our team today.