If you’re a data scientist or looking to get into the industry, you’ve made a great choice. Demand has more than tripled in recent years. And it’s no wonder – data and analytics have the power to significantly improve outcomes for a range of commercial decisions.
Data science is an ever-evolving field; there’s always new knowledge and skills to learn. It’s the perfect career path for problem solvers and offers the opportunity to make a real impact on an organisation.
Our Business Development Manager Jag Tumber previously worked in recruitment for data and analytics. He’s put together 10 hints and tips for securing your first – or next – role:
1) Bring your CV to life.
Businesses want to hire personalities, not numbers – so don’t forget to include your hobbies and interests on your CV. You might not think it, but your interviewers want to know about that time you climbed Snowdon, ran a marathon, or took up a new hobby.
2) Shout about the skills you’ve developed from seemingly unrelated roles.
You’d be surprised by how many people shy away from talking about part-time jobs they worked whilst studying.
Whether you’ve worked as a sandwich artist, bartender or steward, the experience gained from customer-facing roles is invaluable. Whilst they may have no relevance to the technical requirements of a data science role, don’t dismiss the people skills you’ve developed. From advising customers and multitasking, to handling pressured situations and problem solving, these skills are crucial when dealing with stakeholders in business.
3) Make sure your application is seamless.
It’s in your best interest to make the recruitment process as easy as possible for hiring managers. Check your CV includes functional links to Kaggle challenges, your Github profile, and any presentations or talks you’ve delivered, if applicable.
4) Don’t overegg it.
Honesty is always the best policy, so don’t be tempted to overembellish your skills and experiences. For many employers, soft skills are just as valuable as knowledge of data science software and tools. Technical skills can be taught, but honesty, integrity and dedication can’t.
5) Job descriptions may be the same, but the responsibilities can be worlds apart.
A whole range of job titles can be responsible for collecting, organising and analysing data.
It’s worth stressing that roles can share the same title but have completely different requirements – depending on the company size and maturity, and their data science team’s strengths and capabilities. (Some businesses even use existing templates from other live job roles – lazy, right?!)
6) Build your personal brand.
Networking, creating content and attending events isn’t just for businesses – you should be creating a personal brand, too. Take the opportunity to showcase your great work, meet up with fellow practitioners, and share your experiences, opinions and knowledge.
7) Tap into the power of LinkedIn.
Prior to interviewing, accepting an offer, or even applying for a role, it’s worth reaching out to current team members. Find out what it’s like to work for the company, including a typical ‘day in the life of’. You’ll quickly come to understand the team’s values, ways of working and culture. After all, the average human spends 90,000 hours of their life working – so you need to make an informed choice!
8) Hone your data science toolkit.
Programming languages like Python and R tend to be a must for any data science job. However, don’t get too bogged down in learning all possible languages – after all, tools are just a means for implementation. Demonstrating you understand the concept is far more important.
9) Don’t turn up your nose at Excel.
Sure, there are better, faster, more agile programmes that create much fancier representations. Then there’s Excel. But guess what? You’re probably still going to be using it for data science in 2022 and beyond.
Whether you’re examining, mapping or visualising data, for many businesses Excel’s accessibility often makes it the programme of choice.
10) Don’t get hung up over the ‘right’ qualifications.
You don’t need a university degree to become a data scientist. Although most job listings require a Master’s or PhD in engineering, computer science or statistics, it’s possible to learn without an advanced degree or even a Bachelors.
With current levels of demand increasingly outpacing supply, companies are becoming more open to hiring non-traditional applicants.
We’re recruiting for a range of technical roles in our consultancy and product teams. Get in touch for more details and in the meantime, find out more about our brilliant team.