Over the past ten years, there’s been much speculation and anticipation around the rise of data science and analytics. What started out as statistics, and the use of statistical models, has evolved to include many of today’s most sophisticated technologies. Today's ever-changing technological environment presents a range of challenges for data scientists. Here are four of the key issues affecting candidates in this space.
1. What’s in a name? Confusion over job titles
The job title ‘data scientist’ is broad and can often refer to several different careers. As the profession has evolved over the years, so have the job titles. Ten years ago, a data scientist of today may have been coined a ‘business intelligence analyst’ or ‘business modeller’. Now, job titles such as ‘data analytics’, ‘data mining’ and ‘risk modelling’ also commonly fall under this description.
Generally, a data scientist analyses information to learn about scientific processes, market trends and risk management. Data scientists work in a wide range of industries, from tech and medicine, to corporate and government agencies.
The qualifications for a data scientist vary as much as the wide range of titles do.
Job titles that sit within the data scientist field are likely to be a combination of many different roles. Part developer, application builder, mathematician, computer science and programmer.
Employers look for the basic skills, such as strong statistical, analytical and reporting skills. However, the modern data scientists need to marry superior technical skills with business acumen. The best discovery and analysis comes from asking the right questions. Understanding the business and its needs, while delivering solutions in a clear, concise and persuasive manner is also incredibly important.
2. Undersupply of “true” data scientists
When it comes to data scientists, while there’s a skills shortage, there’s no shortage of junior applicants. As with other high-interest tech positions, data science entry-level roles receive hordes of applicants. An individual with a new degree in data science is likely to struggle to find a role.
The shortage of skills exists around the type of well-rounded, experienced data and analytics professionals.
Businesses are struggling to find the specialised data scientists they need to take on the enormity of their data challenges – entry level talent isn’t going to cut it.
According to CIO, the essential skills and traits of elite data scientists comprise the unique combination of technical skills, mathematical know-how, storytelling, and intuition. Experience and the ability to step back and assess a problem or situation is at the core of this.
3. Ethical fears in data world
Last year, Forbes wrote about concerns over lack of ethics and empathy in the digital world, in particular data scientists and programmers and their impacts on privacy. This is a concern echoed by some of Talent’s senior data science candidates. In an accelerating field, where businesses are profiting from their data, for purposes different from those which it was originally collected, can legal frameworks and
regulations keep up?
In an ideal world, private customer data should remain private and anything shared should be treated confidentially. However, Talent’s data scientists are seeing many companies taking knee-jerk reactions to automation and machine learning. Reacting quickly doesn’t guarantee it’s done well. Rapid changes to systems and functions can expose vulnerabilities. Also, many data-related contracts are short-term, meaning companies don’t have the time or resources to invest in ethical considerations. It’s a case of focusing on a specific task, rather than thinking big picture.
As it stands today, there is no framework or code of ethics the data science or analytics community must abide by. Those data scientists, data engineers, database administrators and anyone involved in handling data should have a voice in the ethical discussion on how data is used. Companies should be openly discussing ways to introduce accountability, fairness and transparency in the data space.
4. Diversity in data
Given the role that data scientists play in shaping how data is used, it’s important that all walks of life are fairly represented in the field. Making this happen starts when the industry as a whole commits to hiring and developing diverse talent.
As demand for data scientists’ soars, the profession is in a unique position to responsibly lead the way in diversity. That means data scientists comprising of equal proportions across gender, ethnicity and ability.
According to a recent Harnham report, only 18% of today’s data scientist roles are occupied by females and 11% of data teams don’t have any female representation. While these figures come from the US, it mirrors the situation in Australia and New Zealand. The gender gap extends across all areas of tech, not just data roles.
When it comes to gender diversity, feedback suggests that encouraging young women in STEM subjects – both in secondary and tertiary education - is essential to improve the gender balance over the next decade.