What Data Scientists Do
Data science is a hot career with a presumably bright future, as most companies gather data about the use of their products and/or consumers in order to drive insights, product or service developments and profits.
In order to make sense of the data gathered, organizations turn to data scientists; individuals who identify trends and bits or correlated data, then identify possible causes and create visual representations of the data, so that others can better understand it. Because demand is surging for professionals with the background to perform deep analysis, it’s one of the most popular choices for well-educated people seeking out a mid-career change.
Who would enjoy a career in Data Science?
Data scientists must have strong skills where statistics, mathematics, and computers are concerned, and an analytical mindset is essential. Although many assume this is a desk job , data scientists also have to be good communicators and be visually-oriented as well, because they convert their findings into visualizations and presentations that a company’s decision-makers and laymen can understand, in order for them to be able to take appropriate action based on the information gathered. As an aside to this, those with business acumen, who can truly understand the corporate objectives, are the biggest assets to their teams, and creative people who love puzzles and have some computer coding skills do best.
Who mightn't like the career?
Almost all data scientists have at least a master’s degree and nearly half have a PhD, so the career would likely not be a good fit for someone who was not already well-educated or who wasn’t willing to commit to additional schooling.
Oftentimes, the job can feel a bit like finding a needle in a haystack, as data scientists have to cull through vast amounts of data and clean it (remove all the junk and static) before they can even begin to search for patterns. These steps can seem tedious to those who need constant stimulation.
It’s also worth noting that ethics is a common hot topic within the industry. Professionals will likely be faced with decisions like how much data can be gathered, what methods should be used, and what to do when research produces information that can identify a person, an unpleasant truth, or other difficult situations.
Although most companies have ethics policies in place, they may be more liberal than the employee’s, or they may be so restrictive that the data scientist struggles to complete assignments due to red tape. For this reason, the career is not advisable for anyone who lacks moral fortitude or who is uncomfortable communicating with his employer when gray areas come up.
Having an advanced degree, typically in a STEM subject, is a good start, though candidates should also be familiar with an analytics platform. “R” is presently one of the most popular, as is “SAS,” though others may be expected.
The programming language “Python” is a top skill too. Well-qualified candidates will be able to work with the platforms like Hadoop, Spark, and Flink, be familiar with SQL Database/Coding, and be ok working with unstructured data.
To be clear, there is not a single “best” set of tools a data scientist should know; each has benefits and drawbacks and different tools may be required for individual employers or projects. Knowing a few very well is a good entryway, and this can be built upon by some familiarity in others. To demonstrate proficiency with a particular tool, certification or attendance in an online course is generally adequate.
With data science being an emerging field, there are still some occasional disputes as to what a data scientist does. Before interviewing anywhere, candidates should always double check that their skills are a good match for the position. An interview will likely involve working with an organization’s data set to produce some kind of deliverable, insight or recommendation to the interviewer.
- Questions from Data Science Interviews (Includes Interview Tips)
- 5 Steps to Actually Learn Data Science
- Getting Into Data Science: What You Need to Know
- What's The Best Path To Becoming A Data Scientist?
Moving into Data Science from another career
Data science can be a fantastic transitional career for most anyone with an advanced degree. If a love of analytics and understanding of computers is already present, picking up the extra skills and certifications necessary to secure a job could be done via extra coursework on nights and weekends while staying in one’s initial career. For further reading, see “How to Become a Data Scientist - On Your Own” and “Salary History and Career Path of a Data Scientist,” which covers the 25-year career of Vincent Granville, who is perhaps best-known for creating Analyticbridge.
Role: Unlike many careers in which there is clear linear progression, data scientists generally begin and end their careers while holding the title “data scientist.” Oftentimes, companies like Facebook will not even differentiate levels of experience or pay outwardly.
Though the employee will certainly have a pay grade that increases based on merit, the grade is private between the employee and HR. However, some firms may designate junior and senior roles within teams. Some tech organizations have Manager, Director, VP and even Chief Data Officer titles, certain of which can take on people management or more advanced data science responsibilities such as architecting a data warehouse for a company or speaking publicly on data topics to boost awareness of the organization’s data expertise.
The amount of travel required will vary greatly depending on the company one works for. Large corporations like Google, Facebook, and Amazon tend to keep their data scientists at centralized hubs throughout the world. Companies that employ one or only a small number of data scientists may well expect their employees to travel internationally to anywhere they conduct business.
Entry Level: According to data from PayScale, data scientists beginning their careers have salaries of approximately USD $89,280 in the United States, £34,920 in the United Kingdom, CAD$71,540 in Canada, and AU$93,100 in Australia.
Mid-Career: USD$106,950, £48,960, CAD$84,680, AU$101,920.
Experienced: USD$120,900, £69,840, and CAD$84,680 (no change noted). There is presently no salary data available for experienced data scientists in Australia.
Although smaller companies don’t always support large salaries, big corporations like Amazon, Facebook, and Google rely on their data scientists in a big way, and they pay their employees incredibly well to keep them at work. Data from Glassdoor says Facebook employees in the UK clear £131k - £141k when profit and commission sharing are taken into account. In the US, annual salaries can reach $218,947.
Why Data Scientists move on
At present, data science is a field people are flooding to, but few leave. The career is hailed as “The Sexiest Job in America” and is believed to offer a high level of job security overall.
Moreover, the wages are among the top of almost any profession, so it’s difficult to imagine someone wanting to give all that up, but it does happen. Some may become disenchanted with the moral gray areas, while others aren’t willing to base their futures on an emerging career and make a jump to more established fields for security.
Moreover, although the career, itself, is generally considered a safe bet, competition for positions at big data firms is fierce, and those who don’t make the cut or who intentionally choose to pursue positions with smaller enterprises and startups will likely see their employers close their doors several times throughout their careers.