After years of working on your PhD and being in academia, transitioning into a Data Science career can be a culture shock.
It’s important to understand how the roles you are wanting to apply to vary; what industry is it in? What are the main focus of the projects? Who will you be working alongside? What are the end goals the team would be working on achieving? Does your chosen industry have anything to do with your PhD and academic research?
‘Data Scientist’ is becoming an ever-ambiguous term for many facets of working within the Data Science industry. Not limited to technology, Data Scientists are found in many industries as companies seek professionals to wrangle their data.
When entering the work force following your PhD, recognise what sort of position you want to go for and, will it be linked to your PhD. Play on your strengths here and showcase exactly how your PhD can benefit the team and business as a whole, what impact will you and your work have? This is a must to include in your resume! Find out how you suit the job and what you can offer from looking at the job description in detail. Check out the company website, their social media, and publications they may have. If you’re working with a recruiter, they can tell you more about the company too.
Networking can play a huge part in securing roles. Reach out on social media, email or connect with people you may know at the companies you’re applying for. Show an interest in what they do, find out about the company – it might not be for you on closer inspection, or you might find the perfect fit. Many companies offer referral schemes amongst employees, so you never know – you could even secure a referral by reaching out!
Whilst studying, you tend to have a lot longer to work on projects and research as compared to data science projects within a business team. Companies may put pressure on you for a quicker turn around than academia. Be aware of this when writing up your experience. Outline your technical skills, past projects, and play on the transferable soft skills that companies will be looking for.
Whilst a CV isn’t the place a hiring manager will fully get to grips with your technical ability, it’s a great place to showcase what you can and have worked with. Most job descriptions will contain the type of tech you’ll need experience in, and what the role will require you to work on. If you do need to gain more technical skills before applying, there are plenty of courses out there that you can look into to broaden your skill set.
Going beyond technical skills, your academic interests may not always apply to the business world and that is okay. Just give what examples you can on working with data efficiency and analytics to bolster your application. Remember to start your job search before graduating! It’s a tough market, so get ahead of the rush and reach out to companies in good time.
Once you’re in your new role you may find a lot of the ways you’re used to doing things are different. You’re not in Kansas anymore, Toto. You may find there is less room for experimentation with new algorithms where the tried and tested ones are already being deployed. This is because in a business setting, the end result is the most important thing. With deploying new algorithms comes more risk. If the techniques in place already deliver, you might not get the chance to experiment in these ways.
It’s also important to remember that in industry, things move a lot quicker than in academia. Previously, you may have been able to spend years on a research project. Now you’re in a company, you will need to condense your work and finish off within weeks, sometimes even less. You may also need to compromise, because of this, on smaller sample sizes and costs of experiments to meet business needs.
As with all roles in industry, communication is key. You’re going to need to drop some of the technical language you’re used to communicating to an academic audience. Make it accessible for those not trained in your area of expertise.
Working as a Data Science in industry is a really rewarding path to go down. There are great opportunities to apply your knowledge and skills to real world problems where you can see these making direct impact.
What have your experiences in this transition been?
