Cody Szeto is director of the IT&T, engineering, supply chain and procurement divisions at Kelly Services Hong Kong.
How can I become a data scientist and will it help my work-life balance?
A recent article said that data scientists have the world’s best work-life balance. Considering that I’m currently working 12-hour days as a software developer in an analytics solutions company, I’d like to pursue a career change. What does a data scientist do? And what qualifications or transferable skills do I need in order to pursue such a position?
Data scientists are professionals who generally receive foundational training in computer science and applications, modelling, statistics, and analytics. They are able to combine their technical skills with strong business acumen to spot trends valuable to a company’s business. They are expected to interpret data to tell stories, especially to stakeholders.
Many think that a data scientist is similar to a traditional data-related professional with a system background in customer relationship management. But, in fact, a data scientist sifts through data from various disparate sources to unearth useful information for the company. This can be used to generate effective business solutions that stakeholders can understand and buy into. In short, a data scientist is an analytical business visionary who fully embraces the art and science of data.
I understand that working 12-hour days continuously is exhausting, and that you’d like to attain a better work-life balance. These thoughts of yours may have been inspired by the recent Glassdoor survey which listed the “25 Best Jobs for Work-Life Balance”, with data scientists ranking number one.
To me, these survey results can only be taken as relative benchmarks. In my experience, work-life balance is seldom a homogenous factor by function and is subject to company culture, departmental dynamics, and perhaps even the line manager’s style.
In the very same Glassdoor survey, the work-life balance of software developers was rated almost as highly. However, apparently, it fails to reflect the harsh 12-hour days you are enduring.
Ultimately, there is no perfect job. There are only more suitable jobs–those that capitalise on your strengths and match your interests. If you enjoy your job, you should consider switching to other teams, or a company which offers a better work-life balance.
However, if you still believe that data science is a field you’d like to pursue, you’ll first need good knowledge of statistics, including things like statistical tests, distributions and maximum likelihood estimators. You’ll also need to know a statistical programming language such as R or Python, and a database querying language such as SQL.
You must also understand the various machine learning algorithms for classification – random forests, k-nearest methods and suchlike–and have the skills to work with missing, inconsistent or incomplete data.
Finally, the ability to visualise and communicate data to stakeholders, and having the business sense to determine which products do develop, will give you an advantage.
This article appeared in the Classified Post print edition as Grass is not always greener as a data scientist.