I was having virtual drinks with my friend Anne a few days ago when she said something that will probably sound extremely familiar to some of us.
Though the pandemic has forced her to adapt in order to make ends meet – going from organising VIP experiences at summer music festivals to making masks and garden landscaping – she felt that, somehow, her life is now more aligned with the way she wants to live it. What began as a temporary adaptation, a side project designed to tide her over until the end of the pandemic, has become a model for her future career.
Our conversation got me thinking about the many data scientists I know who, in a similar way, have accidentally found their passion and made a career out of it. The common denominator? Serendipity out of necessity.
Of course, it doesn’t always take a pandemic to make you reevaluate your career path. In the case of many of my colleagues and contacts, it was an unrelated activity like a dissertation, project or thesis, that forced them to learn new ways to work with tremendous amounts of research data. After they accomplished their primary goal and looking back, they came to the conclusion that what they enjoyed the most was dealing with data: collecting it, wrangling it, and learning new techniques to help them use the data to achieve results.
Data science work is challenging and, of course, should not be taken on lightly. But making the choice to pursue a data science career is also incredibly rewarding. Whether you’re thinking of changing career paths or are just beginning to plan out your future after academia, there are four key reasons why you should consider taking the plunge into data science.
You’ll be highly in demand.
In the last couple of years, data scientist has become one of the most in-demand jobs in the market. In stark contrast to academia, where many STEM graduates find themselves competing against hundreds of others for the same handful of roles, there’s a real shortage of data science talent. Your passion could, potentially, become an enjoyable and successful career.
Some of my data science colleagues opted to apply to jobs directly and learn on the job. Others joined programmes like the Faculty Fellowship, which help the best talent transition from academia into commercial data scientists through training in soft and hard data science skills and industry placements.
You can help create change across a huge range of sectors.
Data science is an incredibly varied field.
We use data science most commonly in health & life sciences, retail and consumer, financial services, energy transition & environment and education, but we’re applying AI in new sectors every day. In my view, any industry where there is a problem to be solved and enough related data available is ready to explore what AI has to offer; at Faculty, we have worked in 23 different sectors and the list is growing. The world is your oyster.
It’s entirely up to you whether you want to specialise in one industry, developing deep expertise in the problems, processes and opportunities offered by each sector, or work across a range of industries and tackle a new kind of problem every day. You might want to work in a sector that’s consistently at the cutting edge of AI tech, like financial services, or unlock the power of AI in a sector that’s relatively new to data science, like construction. The applications of data science are so wide that it’s possible to shape your own, unique career.
You can find a team and a working style that suits you.
For the last three years, I have been working with multiple clients’ data science teams. It is amazing to see the different team structures and where data scientists fit in the organisations.
For example, I’ve worked with a global healthcare organisation which uses a decentralised model, with many small data science teams aligned to the multiple business areas across the organisation. The different teams work independently, but they share their work and findings in collective monthly sessions.
One of our airline clients, on the other hand, has a central data science team that works across all areas of the business, from HR and finance to forecasting sales and optimising standby staff.
In short, it’s not just the choice of industries that allows you to ‘customise’ your data science career; you can also choose from a wide range of working styles and areas of focus within an industry. Maybe you prefer a big team to a small one. Maybe you having control and sight over all of the data science initiatives taking place within one company, or maybe you want to focus closely on one particular department and work hand-in-hand with them to solve problems as they arise.
Your day-to-day job role is still very flexible.
There are currently 2000+ data science jobs advertised across the 6 most popular online recruitment sites. I challenge you to read a few job descriptions and compare the roles’ responsibilities. As a data scientist, you could be responsible for the end to end process (project scope and discovery, data engineering, modelling and deployment) or just be focused on the modelling part, working with other specialists in the organisation to implement AI.
Fig. 1: An example of some data science team structures.
People tend to assume that data scientist roles are similar to, for example, payroll officers, with a very defined set of skills and responsibilities that don’t change much between organisations. But, in reality, the title of data scientist is really an umbrella that covers a huge variety of roles, day-to-day tasks and areas of focus. Because data science is still a very new field, and so many data science teams are still in their infancy, there’s still a great deal of flexibility and a real opportunity to find a role that suits your interests and skills.
In summary, now more than ever it’s important to take stock of your abilities and find new ways to develop and define your career. If you, like many of my professional clients and colleagues, have found that data is fascinating and have a good maths’ baseline, I encourage you to explore a career in data science. People who do what they love for a living never work a day in their life…
What’s next? How to get a job in data science
We created the Faculty Fellowship to help aspiring data scientists develop core technical and commercial skills, make industry contacts and win industry placements – all in just eight weeks.
If you’d like to make jump into the data science industry – head to our Faculty Fellowship page to apply .
To find out more about the realities of a career from fellowship alumni working in the field today, check out our roundup of the most interesting insights from our webinar ‘The insider’s guide to getting into data science.’