There is a culture of continuous learning...
We have several data science positions open here at Nova Futur and I wanted to help those looking to apply by gathering some helpful information from both sides of the process; those hiring, and those who have successfully gone through the process and been hired!
Our Head of Data spoke to me about our hiring process and the kinds of skills and qualities we look for in ideal candidates, this is what he had to say:
When looking at potential candidates it is expected that they have a portfolio to present. At least 1 project within their portfolio should show their python skills and ideally it should be on GitHub. This streamlines the selection process significantly as they can accelerate to the interview stage if they can see evidence of good skills and it is a good way to make yourself stand out straight out of the gate.
This is a time-limited test that takes around 3 hours to complete. It consists of general real-world problems that you have to solve. The scenarios have been developed to represent potential problems that will arise in the job but it is not directly related to the work we do as we are trying to provide a fair interview process. The solutions you provide will show if you have the technical skills required for the role, and it gives you the opportunity to show your problem solving and programming skills. On top of this it also gives you the chance to show your documentation skills which include your ability to describe your solution and communicate effectively.
It is encouraged to use resources within reason for the test, after all you are not being tested about how well you can google things, you are being tested on how well you can utilise your skills, problem solve and innovate solutions. You should be up-front and honest about what you do google during the technical test as there is flexibility in what you can and can't research. For example, if you do search the web it is not an immediate deal breaker, you just have to explain what you searched for because one thing is looking up a formula and another is looking up the whole solution.
In terms of the work you produce, it is important that you write well organised and simple code, we are always looking at quality over quantity. You should document your approach succinctly, step by step in clear and concise language, avoiding superfluous information. This will show your level of understanding in your own work. Additionally, remember to include automated testing as this will show that you understand how the solution can be verified to be true, though this is detailed in the task description.
we can teach skills but we cannot teach attitude...
After passing the technical test the interview will be a way of measuring if you will be a good fit for our company and the data science team. Will you mesh well with the pre-existing team? Can you communicate well? When asked about the technical test and potentially challenged about it will you prioritise finding the correct solution or proving that you are right? We work as a team here and solutions are rarely down to the work of just one person. It is usually a collaboration of ideas and we need people who are able to recognise when others are perhaps more correct so they can then expand on an idea that the other person has brought to the table.
Other important things to take into account are personality fit and general friendliness to people outside as well as inside of your immediate team. It is vital you know how to collaborate and communicate with people across different technical backgrounds. A part of the data science job is to make a story out of data that needs to be conveyed to people who do not know the technical side of things, so you need to be able to communicate technical ideas to non-technical people in a clear way.
You must have the appropriate level of skills according to the role being advertised for i.e. junior or senior. Other necessary skills are a basic knowledge of computer operating systems, knowledge of how to make machine learning models available via a rest API, and how to put the machine learning model into production so other teams can interact with it.
The “Nice to Have” qualities are valuable but not as valuable as having an understanding of how they work and how you can learn and apply them. For example, if someone has experience with ‘Big Query’ but their actual skills do not meet our level then that information is only as useful as they are. On the other hand, if their skills are up to the task and they know how “Big Query'' works but have no direct experience with it, that is more helpful and could be transferred to future issues. A non-data example of this is if Uber were hiring someone to be a driver for them and they had one person who could drive and another who could drive and has also had to change a car tyre, the second person has the more valuable skill set and therefore would be hired.
At the end of the day, we can teach skills but we cannot teach attitude. This means that we will always go for the candidate that has the right attitude and who wants to learn, over someone that purely has the skills.
I also spoke to our most recent data science hire about their experience with the process and if they had any insights that might help someone looking to apply.
They first heard about the position online while exploring various job sites, got in contact with our Office Administrator through our website to clear the first few checks like right-to-work and when everything was seen to be satisfactory they were emailed over the technical tests.
The test comes in 2 parts, the first is mandatory and the second is optional depending on knowledge and experience level. After successfully completing the technical test they were given an interview for the role. Some important points they said that should be remembered when applying for the role is to be honest about your skill level. If you are applying for an entry level role, do not worry if you can’t meet all of the extra requirements, these will be taught to you once you enter the role. Ensure you meet the “must haves” and be honest about what it is you can't do because it is easy to teach a person but only if they admit they need to learn. This candidate found the experience to be very objective in this way, the optional requirements were just that, optional.
Since joining, they said that the projects they are working on are extremely interesting and they are certain they have made the right decision to work with Nova Futur. They have found the environment very friendly and supportive, especially starting as a junior member as there is a positive “culture of continuous learning”.
Their overall words of advice were to make sure you have the right skills for the task, and not to be put off if you can’t meet all of the requirements, especially if it is a junior role, just focus on the main ones. Throughout the process it was clear that if you don’t know something the team here will be more than happy to teach you and you can easily learn on the job. And most importantly don’t be scared to apply if you are just starting out, it is an entry level job after all!
To learn more about our open positions within Nova please look at our careers page: https://www.novafutur.com/careers.
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