In today’s data-driven world, data scientists are among the most sought-after specialists. IBM has recently predicted that by 2020 the number of data science job openings will reach 2,720,000. Although the profession in itself has been around for approximately 50 years, it is now in peak demand, with career websites like Glassdoor, Indeed.com, and Monster.com featuring a plethora of open Data Scientist vacancies with the average salaries of about $120,000 (USA).
With the demand for data-driven solutions running high, your organization might just as well be among companies considering enhancing their team with experienced Data Scientists. But what exactly is data science and what’s the difference between data science and data analytics? Below we will examine the specifics of Data Scientist profession and look at the job skills typically expected from a qualified data science expert.
What is Data Science?
If you Google the definition of “data science”, you will instantly see a Wikipedia article defining it as “a multidisciplinary field that uses a range of scientific processes, extract insights from structured and unstructured data.” Techopedia refers “data science” as the use of “theoretical, mathematical, computational and other practical methods of evaluating data and techniques like data mining, big data analysis, data extraction, and retrieval.” It further mentions data engineering, statistics, programming, social engineering, data warehousing, machine learning/deep learning (ML/DL), and natural language processing (NLP) as sources of data science concepts and processes.
This looks confusing and, in fact, data science is frequently confused with data analytics and machine learning. In short, data science is a broader term encompassing fields like data analytics and ML, among many others. Although associated with cutting edge technologies, the area in itself isn’t new. Formerly referred to as “business intelligence”, “predictive modeling” and “statistics”, it has now been rebranded as “data science” to attract job seekers.
Data Science Salaries and Earning Opportunities
In 2012, Harvard Business Review called data science “the sexiest job of the 21st century”. If money is sexy, then they’re probably right. As reported by Glassdoor, an average Data Scientist salary in the US is $117, 345. It will differ depending on the country or state, at times, quite dramatically. Below are the annual figures provided by Payscale and Glassdoor:
- In the United Kingdom, an average data scientist’s salary is £47,978 per annum;
- In Germany, it constitutes €61,548;
- In Israel, an entry-level salary is as high NIS 282,999 ($78,260).
Of course, not only the region of residence is a main salary formation factor, but also overall experience and expertise, as well as an education degree: in general, graduates of reputable colleges and universities command larger salaries.
How Can Data Scientists Prove Own Value To Senior Leadership?
All-in-all, here’s what the most in-demand, and, thus, best-paid data scientists, can contribute to their professional skills:
Unique tech expertise
When data scientists gain expertise in a certain technology, for example, NLP, it usually increases their market value.
Experience in the booming, trendy industry sector
Some fast-developing fields, such as robotics or computer vision, experience a dire need for data scientists with relevant expertise.
While some organizations will look for experts in niche industries, others will welcome professionals experienced in many areas. The idea is that a more versatile professional would introduce a new and better vision.
Participation in international projects
All in all, exposure to different cultures is the best way for data scientists to widen their horizons, enhance communication skills, and understand cross-cultural differences.
Understandably, such knowledgeable, well-rounded professionals are extremely hard to find. So far, where to find a good data scientist, remains an open question. So how do companies handle skills shortage and where do they source data science talent?
Ways To Source Data Science Talent
To attract data science specialists, companies leverage both passive and proactive techniques. In terms of creating the conditions to grow and nurture their own talent, some organizations get truly creative. Passive methods, like advertising on job websites, also work. However, sticking to them alone can be limiting.
This tried-and-true method is still widely applied by many corporate HRs. A lot of them complain, though, that despite getting numerous responses to their job offerings, few of these responses are really relevant. It does enable companies to come across quite a few motivated candidates, although for some people, describing their experience may not be their strong point.
Data Science contests and hackathons
Data Science is actually a competitive sport. There are many web platforms used for holding data science contests today. The best-known one is Kaggle which enables you to launch your own contests. To enrich their access to talent pools, companies keep a close eye on winners and participants and send job offers to the most promising ones. Apart from contest websites, headhunters also look for potential candidates on different Data Science forums and communities such as Data Science Central, KDnuggets, Revolution Analytics, and many more.
Referrals and word of mouth
A lot of HR experts report actively using internal recommendations to find qualified data scientists. Some offer remunerations to existing employees for recommending prospective candidates. This method often works best, since people, generally, tend to know each other within a certain industry or niche.
Workshops and lectures
Giving something out for free is another actionable recruitment method. Some companies hold workshops and lectures for interested professionals and get resumes and job applications in return. This is one of the means of attracting motivated talent with great data scientist skills and a high level of engagement.
Attracting young candidates by offering them paid or unpaid internships is also one of the best recruitment practices. Some companies never actually hire anyone without an internship: the best possible way to tell how prospects will handle the tasks and integrate into a corporate environment.
Partnering with universities/colleges
A lot of organizations choose to grow their own talent by winning over young data scientists while they are still in college. By helping them make their first steps into the profession, these firms create long-lasting loyalty. This is a long-term strategy, though, requiring close cooperation between a company and a college or university.
Outsourced Big Data R&D
Because of the local tech talent shortage, some companies opt for the most obvious way to gain access to broader pools of data science talent: offshore outsourcing. It’s no secret that Eastern Europe, Ukraine specifically, is a thriving hub for multi-faceted tech talent. The country boasts a high level of professional education with about 30,000 tech specialists graduating each year. Not only is outsourcing one of the obvious means to access high-quality talent; with Ukraining data-science salaries of $41,400 per year, it’s also one of the ways to reduce staffing expenses.
What To Pay Attention To When Hiring Data Science Talent?
Despite lamenting talent shortage, HR consultants also claim that the market is overheated: too many people apply with no relevant knowledge or expertise attracted by high salaries and benefits. Apart from accessing former experience (if applicable), these are the things to pay attention to when hiring data scientists:
Relevant education will give candidates a solid background for further development. A lot of companies that hire data scientists, actually value it more than the previous experience.
Having a portfolio of successfully completed projects will always have a positive impact.
If the candidates have successfully completed the test assignment, it could give them more credit than a portfolio and previous employment history. A test task helps HRs better understand the candidates’ knowledge level, and see how they handle company-specific tasks.
Data science is now a buzzword; admittedly, some candidates are only there for the hype. To make sure candidates are truly interested in the profession, recruiters assess if they are enrolled in any extra educational programs for honing their skills. This will show if they strive towards professional improvement or not really.
Like-minded people usually work best. What most recruiters will surely pay attention to when looking for data scientists is how interested a candidate is in a particular industry or business field. For startups willing to create a team of equally dedicated individuals, shared passion towards a project is highly important.
To wrap up, hiring qualified data scientists is still quite a challenge today. Given the apparent data science talent shortage, you may feel the standards are too high. Settling for less educated, knowledgeable and motivated specialists, however, won’t help. On the contrary, it may jeopardize your project and negatively impact your company reputation. Regardless of which method you ultimately choose to recruit data science talent, relevant education, solid knowledge base, high level of professionalism and shared values should be your priority.