Working in Data Science recruitment, we’re no strangers to the mountains you have to climb and pitfalls faced when getting into a Data Science career. Despite the mounting demand for Data Science professionals, it’s still an extremely difficult career path to break into.
The most common complaints we see from candidates who have faced rejection are lack of experience, education level requirements, lack of opportunities for Freshers, overly demanding and confusing job role requirements.
First of all, let’s tackle what seems to be what seems the hardest obstacle to overcome, lack of experience.
This is a complex one and not just applicable to Data Science, across professions it’s a common complaint that entry-level jobs ask for years’ worth of experience. Every company wants an experienced data scientist, but with the extremely fast emergence of the field and growing demand for professionals, there is not enough to go around!
Our advice here for anyone trying to get into Data Science who is lacking experience is to try and get an internship by contacting companies directly. Sometimes, you will find these types of positions available with recruiters but you will no doubt have more luck going direct.
Another approach is to have a go at Kaggle competitions, write code and put this on GitHub for people to see. There are many ways you can gain experience in your spare time without this being in a business setting, in a way that a hiring manager will notice. If you have the time free too, think of offering free consultations to friends or businesses and build on opportunities like that. Go beyond publishing code on GitHub, and write a detailed post of your analysis and code on a blog, data site or even LinkedIn. This gives you even more exposure and exemplifies your deep understanding of what you do.
There are also challenges for people with heaps of experience getting rejected due to ‘lack of experience’ and the truth is, is that lack of experience often translates to you have a lack of applicable experience to the role you’re applying for. To overcome these obstacles, make sure you’re reading job descriptions properly, researching the company and tailoring your resume to highlight how you are what they’re looking for.
Deciphering Job Descriptions
The growing demand for Data Scientists in a number of different industries, specializing in different fields means that it can be difficult for employers to define a reasonable, ‘blanket’ skill set required, which can lead to a lot of confusion for those starting out. Beyond knowing that a good Data Scientist needs to be a critical thinker, analytically minded, a great communicator and have a passion for the field, technical requirements and experience needed can vary greatly between roles and companies.
Try not to be overwhelmed when looking at job descriptions. It’s important to remember that many companies will put on more skills and experience than actually needed into the job descriptions. So, even if you hold half of the skills they’re asking for, but make up for the rest in willingness to learn/passion for the role/transferable skills, then go for it – don’t be put off. If you’re not confident in doing so, try seeing the patterns in what is being asked for, highlighting the top required skills for the roles you want to apply for and take some time in getting better at these.
Many professionals, whilst having the qualifications needed, lack basic skills needed when it comes to communicating with hiring managers and recruiters.
Commenting on LinkedIn posts asking for a review of your profile is not going to cut it, I’m afraid. Reach out directly to those that are posting the job adverts or if it’s a company, do some research and find the hiring manager or recruitment team. They’ll appreciate the direct approach, and you’ll be able to provide more information on why you should be considered for the role. It might seem like a good way to get noticed as CV’s can get lost in the mountains that recruiters receive… but this is where resume skills come in to play and knowing how to get yours noticed.
You’ve more than likely got some great points on your CV, experiences, and projects that are noteworthy but often, your CV will also be littered with irrelevant information to pad it out – especially if you’re just starting out. Our advice? Get rid of the filler, get to the point and highlight how you can make a difference where you’re applying to.
Make sure your skills, experience, and projects tell the hiring manager that you have the tools necessary to make an impact on their business and how when applying these techniques in the past, you’ve had x y z results. Quantify these results – how did it benefit the company in terms of revenue, ROI, time-saving or costs?
Tailor your CV, don’t just send generic ones out. Exhibit your understanding of the fundamentals, that you have proficient knowledge of the foundations of data science and the rest will follow. The layout is also important, hire a designer or put in some hours on free platforms out there that can help with this. Even on Word, you can create an interesting, eye-catching layout! You can see more on mastering your resume here.
Another great way to soak up as much information about Data Science is to follow influencers in your field on social media, especially LinkedIn – there are often really insightful posts, you can reach out to the data science community, learn new things, post questions and see current opportunities available.
Matt Reaney, Founder