Working in data science recruitment, we’re no strangers to the mountains you have to climb and the 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.
We find the most common application rejections stem from:
- A lack of experience.
- Not meeting the education level requirements.
- Not enough opportunities for Freshers.
- The role requirements are often overly demanding and confusing.
Experience
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 the data science market; across professions employers are advertising ‘entry-level’ jobs, yet ask for years of experience. Everyone wants an experienced Data Scientist, but with the growing professional demand, there is simply not enough to go around!
Our advice here for anyone trying to get into data science is to try and get an internship by contacting companies directly. Sometimes, you will find these types of positions are available with data science recruiters, but you will no doubt have more luck going direct and starting your career.
There are many ways you can gain experience personally, in a way that hiring managers will notice. Have a go at Kaggle competitions, write code and share this on GitHub for people to see. Consider GitHub like an online portfolio, where you are able to showcase your skills and knowledge through code. If you have the time, think of offering free consultations to friends or businesses and build on those opportunities. You can even 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.
In some instances, you might already have many professional/personal years of experience behind you. If this is the case, the roles you are applying for are indicating you have a lack of applicable experience. To overcome these obstacles, make sure you’re reading job descriptions properly. You should also research the company and tailor your resume to highlight how you are exactly what they’re looking for.
Deciphering Job Descriptions
The growing demand for Data Scientists in different industries means that employers don’t always define a reasonable, ‘blanket’ set of requirements. This can lead to a lot of confusion for those starting out. A good Data Scientist needs to be:
- A critical thinker.
- Analytically minded.
- A great communicator.
- Passionate about the field.
- Meeting technical requirements.
- Experienced.
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 you would need day-to-day. So, even if you hold half of the skills they’re asking for, but you can show you 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. Highlight the top skills required and take some time in getting better at these for your career aspirations.
Reaching out
Many professionals have the qualifications needed but lack the ability to communicate 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. If it’s a company, do some research and find the hiring manager or data science recruitment team. They’ll appreciate the direct approach, and you can discuss why you are right for the role. It might seem like a good way to get noticed as CV’s can sometimes get lost in the mountains that recruiters receive… but this is where your resume skills come into play.
Resume skills
You’ve more than likely got some great points on your CV, experiences, and noteworthy projects but often, your CV is also littered with irrelevant information to pad it out – especially if you’re just starting out in your career. 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.
